{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Mosul LGBT\Mosul LGBT Aug-Oct 2021\ISIS Victimization\CPS\CPS replication instructions\CPS replication log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Dec 2024, 14:50:48

{com}. do "C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Mosul LGBT\Mosul LGBT Aug-Oct 2021\ISIS Victimization\CPS\CPS replication instructions\CPS replication do file.do"
{txt}
{com}. *The Recognition of Shared Suffering after Violence: ISIS Victimization and LGBT+ Support in Mosul Iraq
. 
. *Replication Instructions
. 
. *Phillip Ayoub, Vera Mironova, Sam Whitt
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the dataset "CPS replication data.dta". Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. See also the dofile "CPS replication do file". 
. 
. *Note: You may need to install STATA packages for the cibar, cem and iebaltab commands. Use findit with the command name to identify and download the appropriate packets to install. 
. *Note: In addition, some graphs require additional formatting using filename.grec files with the graph play command. To format a graph, simply run the command to generate the graph in the do file in STATA, then open the "Graph Editor" in STATA and click on the GREEN "Play Recording" button, then select "Browse" to select the grec file from the folder "grec files for STATA graph formatting" among Replication files. The name of the grec file is indicated in the note below the graph command in the do file for the specific graph you wish to format. This should automatically format the graph, which you may then save to a location of your choosing.
. 
. *"Stata user generated commands to install for replication purposes"
. 
. *"cibar"
. 
. ssc install cibar, replace
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"iebaltab from ietoolkit"
. 
. ssc install ietoolkit, replace
{txt}checking {hilite:ietoolkit} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *cem
. 
. ssc install cem, replace
{txt}checking {hilite:cem} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. 
. *Manuscript Replication
. 
. *In-text replication
. 
. *Sampling took place in two waves in 2021 between August 5-September 4 with an initial 300 respondents and again between October 1-8 with a boost sample of 600 additional respondents, for a total of 900 participants in the study.
. 
. tab datecode if wave==1

       {txt}date {c |}
    encoded {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     8/5/21 {c |}{res}         20        6.67        6.67
{txt}     8/6/21 {c |}{res}         20        6.67       13.33
{txt}     8/7/21 {c |}{res}         15        5.00       18.33
{txt}     8/8/21 {c |}{res}         15        5.00       23.33
{txt}     8/9/21 {c |}{res}         15        5.00       28.33
{txt}    8/10/21 {c |}{res}         15        5.00       33.33
{txt}    8/15/21 {c |}{res}         10        3.33       36.67
{txt}    8/16/21 {c |}{res}         10        3.33       40.00
{txt}    8/17/21 {c |}{res}         10        3.33       43.33
{txt}    8/18/21 {c |}{res}         10        3.33       46.67
{txt}    8/19/21 {c |}{res}         10        3.33       50.00
{txt}    8/20/21 {c |}{res}         10        3.33       53.33
{txt}    8/21/21 {c |}{res}         10        3.33       56.67
{txt}    8/22/21 {c |}{res}         10        3.33       60.00
{txt}    8/23/21 {c |}{res}         10        3.33       63.33
{txt}    8/24/21 {c |}{res}         10        3.33       66.67
{txt}    8/25/21 {c |}{res}         10        3.33       70.00
{txt}    8/27/21 {c |}{res}         10        3.33       73.33
{txt}    8/28/21 {c |}{res}         10        3.33       76.67
{txt}    8/29/21 {c |}{res}         10        3.33       80.00
{txt}    8/30/21 {c |}{res}         14        4.67       84.67
{txt}     9/1/21 {c |}{res}         13        4.33       89.00
{txt}     9/2/21 {c |}{res}         14        4.67       93.67
{txt}     9/3/21 {c |}{res}         10        3.33       97.00
{txt}     9/4/21 {c |}{res}          9        3.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        300      100.00
{txt}
{com}. tab datecode if wave==2

       {txt}date {c |}
    encoded {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    10/1/21 {c |}{res}         74       12.33       12.33
{txt}    10/2/21 {c |}{res}         74       12.33       24.67
{txt}    10/3/21 {c |}{res}         74       12.33       37.00
{txt}    10/4/21 {c |}{res}         76       12.67       49.67
{txt}    10/5/21 {c |}{res}         74       12.33       62.00
{txt}    10/6/21 {c |}{res}         74       12.33       74.33
{txt}    10/7/21 {c |}{res}         78       13.00       87.33
{txt}    10/8/21 {c |}{res}         76       12.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        600      100.00
{txt}
{com}. 
. *When we asked respondents in the LGBT+ victimization treatment about whether they were previously aware of or saw ISIS executing LGBT+ people, only 3 out of 900 indicated yes…
. 
. tab heardgaykilling

   {txt}Have you {c |}
 ever heard {c |}
 about ISIS {c |}
  executing {c |}
 gay people {c |}
   before?  {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        yes {c |}{res}          3        0.33        0.33
{txt}         no {c |}{res}        897       99.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Women make up 22% of both groups. The average participant age was 27, with a range from 18 to 60.
.  
. tab gender

{txt}Gender, 0 = {c |}
      male, {c |}
   1=female {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        700       77.78       77.78
{txt}          1 {c |}{res}        200       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. sum age

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        898    27.41871    6.995505         18         60
{txt}
{com}. 
. *The LGBT+ victimization prime was randomized such that half (450) of the sample received the priming treatment and half (450) represented a control group.
. 
. tab victimorder

   {txt}1 = ISIS {c |}
victimizati {c |}
  on before {c |}
other LGBT+ {c |}
  0 = after {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        450       50.00       50.00
{txt}          1 {c |}{res}        450       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. 
. *First, we consider support for government protection of LGBT+ people from violence. Overall opposition to such protections dropped remarkably in the treatment group from 64.3% (strongly and somewhat disagree) to 29.1%, a reduction of more than half. Similarly, overall support for government protections from violence rose from 35.7% to 70.9%. 
. 
. tab revprotectgay if victimorder==0

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         43        9.56        9.56
{txt}somewhat disagree {c |}{res}        246       54.67       64.22
{txt}   somewhat agree {c |}{res}        100       22.22       86.44
{txt}   strongly agree {c |}{res}         61       13.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revprotectgay if victimorder==1

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          6        1.33        1.33
{txt}somewhat disagree {c |}{res}        125       27.78       29.11
{txt}   somewhat agree {c |}{res}        303       67.33       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. 
. *We find similar results when comparing support for basic human rights for LGBT+ people. Overall opposition dropped in the treatment group from 56.7% to 30.3%, while support rose from 43.5% to 69.8%. 
. 
. tab revhumanrights if victimorder==0

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         45       10.00       10.00
{txt}somewhat disagree {c |}{res}        209       46.44       56.44
{txt}   somewhat agree {c |}{res}        136       30.22       86.67
{txt}   strongly agree {c |}{res}         60       13.33      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revhumanrights if victimorder==1

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          7        1.56        1.56
{txt}somewhat disagree {c |}{res}        129       28.67       30.22
{txt}   somewhat agree {c |}{res}        298       66.22       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. 
. *Many respondents reported property loss (65.8%) through looting and/or confiscation by ISIS forces as well as being forced to flee their homes (39.2%). 
. 
. sum fledhomeisis homelootedisis 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fledhomeisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}homelooted~s {c |}{res}        900    .6577778    .4747174          0          1
{txt}
{com}. 
. *A surprisingly high number of respondents relayed being punished (39.2%) or detained/imprisoned by ISIS (32.4%). 
. 
. sum punishedisis imprisonedisis 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
punishedisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}imprisoned~s {c |}{res}        900    .3244444    .4684272          0          1
{txt}
{com}. 
. *Almost half (47.6%) disclosed that a family member was punished by ISIS during the occupation and that women in particular experienced abuse or were assaulted in some way by ISIS (58.3%). 
. 
. sum fampunishedisis womenabusedisis

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
fampunishe~s {c |}{res}        900    .4755556    .4996798          0          1
{txt}womenabuse~s {c |}{res}        900    .5833333    .4932808          0          1
{txt}
{com}. 
. *The mean number of items selected was 3.24 with a standard deviation of 1.94, indicating variance in overall victimization experiences.
. 
. sum addvictimindex

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
addvictimi~x {c |}{res}        900    3.242222    1.939346          0          6
{txt}
{com}. 
. *We utilize a simple dichotomous variable coded 0 for no reported victimization (16%) and 1 for any form of victimization by ISIS (84%).
. 
. tab dvictimindex

{txt}0 = no isis {c |}
vicitmizati {c |}
on, 1 = any {c |}
       isis {c |}
victimizati {c |}
         on {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        144       16.00       16.00
{txt}          1 {c |}{res}        756       84.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. 
. *In Table 3, Model 1.1 indicates a significant treatment effect of priming on LGBT+ victimization (Treatment) on support for LGBT+ protections resulting in a moderate increase in support from the control condition (Cohen's d=0.47),…
. 
. esize twosample revprotectgay, by(victimorder) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                              victimorder==0 =        450
                              victimorder==1 =        450
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.4717923{col 34}{space 3}-.6044167{col 46}{space 3}-.3388705
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.4713981{col 34}{space 3}-.6039117{col 46}{space 3}-.3385874
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}769.3163
{txt}
{com}. 
. *We see similar results in Model 1.2 for Victimization priming on support for basic human rights (Cohen's d=0.35).
. 
. esize twosample revhumanrights, by(victimorder) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                              victimorder==0 =        450
                              victimorder==1 =        450
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.3480425{col 34}{space 3}-.4797428{col 46}{space 3}-.2161207
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.3477517{col 34}{space 3} -.479342{col 46}{space 3}-.2159401
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}772.7201
{txt}
{com}. 
. *Consistent with H1, we find evidence of a shared suffering effect, where support for protection from violence and basic human rights is elevated among victims of violence who were exposed to the LGBT+ victimization treatment (the Txt x Victim interaction term, Cohen's d=0.71, 0.58 respectively). 
. 
. esize twosample revprotectgay, by(dsharedsuffering) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                         dsharedsuffering==0 =        557
                         dsharedsuffering==1 =        343
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.7097639{col 34}{space 3}-.8480677{col 46}{space 3}-.5710839
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.7091709{col 34}{space 3}-.8473592{col 46}{space 3}-.5706068
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}892.5882
{txt}
{com}. esize twosample revhumanrights, by(dsharedsuffering) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                         dsharedsuffering==0 =        557
                         dsharedsuffering==1 =        343
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}  -.58342{col 34}{space 3}-.7204732{col 46}{space 3}-.4460529
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.5829326{col 34}{space 3}-.7198713{col 46}{space 3}-.4456802
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}894.9064
{txt}
{com}. 
. *The modal response was 0 = not close at all, chosen by 62.4% of the respondents in the control group. However, the distribution was wide-ranging among the remaining 37.6%, who expressed at least some sense of closeness. 
. 
. tab closegay

{txt}How close do you {c |}
         feel to {c |}
  gay/homosexual {c |}
       people?   {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
not at all close {c |}{res}        532       59.11       59.11
{txt}               2 {c |}{res}         68        7.56       66.67
{txt}               3 {c |}{res}         95       10.56       77.22
{txt}               4 {c |}{res}         54        6.00       83.22
{txt}               5 {c |}{res}         59        6.56       89.78
{txt}               6 {c |}{res}         44        4.89       94.67
{txt}               7 {c |}{res}         25        2.78       97.44
{txt}               8 {c |}{res}         16        1.78       99.22
{txt}               9 {c |}{res}          7        0.78      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Figure 4 indicates that feelings of closeness to LGBT+ people are elevated in the treatment group receiving LGBT+ victimization priming (see also Appendix Figure 1). There, fewer people report no closeness at all to gay people (55.8%), and more express at least some degree of closeness (44.2%). Taken another way, the empathy gap between "none at all" and "at least some" is more than halved among those who are made aware of LGBT+ suffering (11.6%) relative to control (24.8%). 
. 
. tab closegay if victimorder==0

{txt}How close do you {c |}
         feel to {c |}
  gay/homosexual {c |}
       people?   {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
not at all close {c |}{res}        281       62.44       62.44
{txt}               2 {c |}{res}         52       11.56       74.00
{txt}               3 {c |}{res}         22        4.89       78.89
{txt}               4 {c |}{res}         27        6.00       84.89
{txt}               5 {c |}{res}         28        6.22       91.11
{txt}               6 {c |}{res}         22        4.89       96.00
{txt}               7 {c |}{res}         11        2.44       98.44
{txt}               8 {c |}{res}          5        1.11       99.56
{txt}               9 {c |}{res}          2        0.44      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        450      100.00
{txt}
{com}. tab closegay if victimorder==1

{txt}How close do you {c |}
         feel to {c |}
  gay/homosexual {c |}
       people?   {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
not at all close {c |}{res}        251       55.78       55.78
{txt}               2 {c |}{res}         16        3.56       59.33
{txt}               3 {c |}{res}         73       16.22       75.56
{txt}               4 {c |}{res}         27        6.00       81.56
{txt}               5 {c |}{res}         31        6.89       88.44
{txt}               6 {c |}{res}         22        4.89       93.33
{txt}               7 {c |}{res}         14        3.11       96.44
{txt}               8 {c |}{res}         11        2.44       98.89
{txt}               9 {c |}{res}          5        1.11      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        450      100.00
{txt}
{com}. 
. *Tables and Figures
. 
. *Figure 1 (see "Figure 1 WVS data.xls" excel file for details)
. 
. *Table 2. Summary of Sample Demographics by Control/Treatment Group
. 
. sum gender age education income i.employment i.religion i.ethnicity if victimorder==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        450    .2222222    .4162024          0          1
{txt}{space 9}age {c |}{res}        448    27.26116    6.173561         18         52
{txt}{space 3}education {c |}{res}        450    3.933333    .3058451          2          4
{txt}{space 6}income {c |}{res}        450    3.037778    .4230491          1          4
{txt}{space 12} {c |}
{space 2}employment {c |}
employer/~r  {c |}{res}        450         .04    .1961773          0          1
{txt}{hline 13}{c +}{hline 57}
professio..  {c |}{res}        450    .1911111     .393614          0          1
{txt}office wo..  {c |}{res}        450    .0555556    .2293164          0          1
{txt}manual wo..  {c |}{res}        450    .2066667    .4053649          0          1
{txt}farmer: h..  {c |}{res}        450    .0155556    .1238858          0          1
{txt}agricultu..  {c |}{res}        450    .0111111    .1049387          0          1
{txt}{hline 13}{c +}{hline 57}
armed for..  {c |}{res}        450    .0177778    .1322899          0          1
{txt}{space 1}unemployed  {c |}{res}        450    .0244444    .1545963          0          1
{txt}{space 4}student  {c |}{res}        450    .4377778    .4966654          0          1
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 6}Sunni  {c |}{res}        450    .9955556    .0665924          0          1
{txt}{space 7}Shia  {c |}{res}        450    .0044444    .0665924          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Arab  {c |}{res}        450         .84    .3670141          0          1
{txt}{space 7}Kurd  {c |}{res}        450    .0955556     .294308          0          1
{txt}{space 4}Turkmen  {c |}{res}        450    .0422222    .2013196          0          1
{txt}{space 10}4  {c |}{res}        450    .0222222    .1475696          0          1
{txt}
{com}. sum gender age education income i.employment i.religion i.ethnicity if victimorder==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        450    .2222222    .4162024          0          1
{txt}{space 9}age {c |}{res}        450    27.57556    7.731279         18         60
{txt}{space 3}education {c |}{res}        450    3.373333    .7510646          1          4
{txt}{space 6}income {c |}{res}        450    3.193333    .8470833          1          4
{txt}{space 12} {c |}
{space 2}employment {c |}
employer/~r  {c |}{res}        450    .0177778    .1322899          0          1
{txt}{hline 13}{c +}{hline 57}
professio..  {c |}{res}        450         .12    .3253232          0          1
{txt}office wo..  {c |}{res}        450    .0822222    .2750087          0          1
{txt}manual wo..  {c |}{res}        450         .26    .4391224          0          1
{txt}farmer: h..  {c |}{res}        450    .0288889    .1676807          0          1
{txt}agricultu..  {c |}{res}        450         .08    .2715951          0          1
{txt}{hline 13}{c +}{hline 57}
armed for..  {c |}{res}        450    .0333333    .1797053          0          1
{txt}{space 1}unemployed  {c |}{res}        450    .0288889    .1676807          0          1
{txt}{space 4}student  {c |}{res}        450    .3488889    .4771492          0          1
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 6}Sunni  {c |}{res}        450    .9066667    .2912228          0          1
{txt}{space 7}Shia  {c |}{res}        450    .0933333    .2912228          0          1
{txt}{hline 13}{c +}{hline 57}
{space 12} {c |}
{space 3}ethnicity {c |}
{space 7}Arab  {c |}{res}        450    .8666667     .340313          0          1
{txt}{space 7}Kurd  {c |}{res}        450    .1288889    .3354499          0          1
{txt}{space 4}Turkmen  {c |}{res}        450    .0044444    .0665924          0          1
{txt}
{com}. 
. *Figure 2 (see "Figures 2-4 data.xls" excel file for formatting details)
. 
. tab revprotectgay if victimorder==0

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         43        9.56        9.56
{txt}somewhat disagree {c |}{res}        246       54.67       64.22
{txt}   somewhat agree {c |}{res}        100       22.22       86.44
{txt}   strongly agree {c |}{res}         61       13.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revprotectgay if victimorder==1

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          6        1.33        1.33
{txt}somewhat disagree {c |}{res}        125       27.78       29.11
{txt}   somewhat agree {c |}{res}        303       67.33       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revhumanrights if victimorder==0

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         45       10.00       10.00
{txt}somewhat disagree {c |}{res}        209       46.44       56.44
{txt}   somewhat agree {c |}{res}        136       30.22       86.67
{txt}   strongly agree {c |}{res}         60       13.33      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revhumanrights if victimorder==1

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          7        1.56        1.56
{txt}somewhat disagree {c |}{res}        129       28.67       30.22
{txt}   somewhat agree {c |}{res}        298       66.22       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. 
. *Figure 3 (see "Figures 2-4 data.xls" excel file for formatting details)
. 
. sum punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fledhomeisis homelootedisis womenabusedisis

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
punishedisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}fampunishe~s {c |}{res}        900    .4755556    .4996798          0          1
{txt}{space 1}injuredisis {c |}{res}        900    .1322222    .3389205          0          1
{txt}faminjured~s {c |}{res}        900    .0833333    .2765391          0          1
{txt}famkilledi~s {c |}{res}        900    .2011111    .4010538          0          1
{txt}{hline 13}{c +}{hline 57}
imprisoned~s {c |}{res}        900    .3244444    .4684272          0          1
{txt}fledhomeisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}homelooted~s {c |}{res}        900    .6577778    .4747174          0          1
{txt}womenabuse~s {c |}{res}        900    .5833333    .4932808          0          1
{txt}
{com}. 
. *Table 3
. 
. reg revprotectgay victimorder dvictimindex wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(3, 29)          =  {res}     7.05
                                                {txt}Prob > F          = {res}    0.0011
                                                {txt}R-squared         = {res}    0.0870
                                                {txt}Root MSE          =    {res} .69443

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .3887539{col 26}{space 2} .0886077{col 37}{space 1}    4.39{col 46}{space 3}0.000{col 54}{space 4} .2075307{col 67}{space 3} .5699771
{txt}dvictimindex {c |}{col 14}{res}{space 2} .3562752{col 26}{space 2} .1204115{col 37}{space 1}    2.96{col 46}{space 3}0.006{col 54}{space 4}  .110006{col 67}{space 3} .6025444
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.1724629{col 26}{space 2} .0986271{col 37}{space 1}   -1.75{col 46}{space 3}0.091{col 54}{space 4}-.3741779{col 67}{space 3} .0292522
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.358234{col 26}{space 2} .2067103{col 37}{space 1}   11.41{col 46}{space 3}0.000{col 54}{space 4} 1.935464{col 67}{space 3} 2.781004
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder dvictimindex wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(3, 29)          =  {res}     7.61
                                                {txt}Prob > F          = {res}    0.0007
                                                {txt}R-squared         = {res}    0.0473
                                                {txt}Root MSE          =    {res}  .7093

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .2921608{col 26}{space 2} .0716205{col 37}{space 1}    4.08{col 46}{space 3}0.000{col 54}{space 4} .1456804{col 67}{space 3} .4386411
{txt}dvictimindex {c |}{col 14}{res}{space 2} .2781763{col 26}{space 2} .1317752{col 37}{space 1}    2.11{col 46}{space 3}0.044{col 54}{space 4} .0086658{col 67}{space 3} .5476869
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.0550717{col 26}{space 2} .0697396{col 37}{space 1}   -0.79{col 46}{space 3}0.436{col 54}{space 4}-.1977053{col 67}{space 3} .0875618
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.305371{col 26}{space 2} .1812522{col 37}{space 1}   12.72{col 46}{space 3}0.000{col 54}{space 4} 1.934669{col 67}{space 3} 2.676074
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder##dvictimindex wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(4, 29)          =  {res}     8.07
                                                {txt}Prob > F          = {res}    0.0002
                                                {txt}R-squared         = {res}    0.1207
                                                {txt}Root MSE          =    {res} .68186

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.3418322{col 38}{space 2} .2274456{col 49}{space 1}   -1.50{col 58}{space 3}0.144{col 66}{space 4}-.8070108{col 79}{space 3} .1233463
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.2521804{col 38}{space 2} .2062886{col 49}{space 1}   -1.22{col 58}{space 3}0.231{col 66}{space 4}-.6740879{col 79}{space 3} .1697271
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .8343203{col 38}{space 2} .2515986{col 49}{space 1}    3.32{col 58}{space 3}0.002{col 66}{space 4} .3197435{col 79}{space 3} 1.348897
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2} -.110219{col 38}{space 2} .0962328{col 49}{space 1}   -1.15{col 58}{space 3}0.261{col 66}{space 4}-.3070372{col 79}{space 3} .0865992
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.812922{col 38}{space 2} .2469607{col 49}{space 1}   11.39{col 58}{space 3}0.000{col 66}{space 4}  2.30783{col 79}{space 3} 3.318013
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder##dvictimindex wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(4, 29)          =  {res}     9.31
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0909
                                                {txt}Root MSE          =    {res} .69328

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.5382158{col 38}{space 2} .2758514{col 49}{space 1}   -1.95{col 58}{space 3}0.061{col 66}{space 4}-1.102395{col 79}{space 3} .0259638
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.4133879{col 38}{space 2} .2526587{col 49}{space 1}   -1.64{col 58}{space 3}0.113{col 66}{space 4} -.930133{col 79}{space 3} .1033571
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .9482797{col 38}{space 2} .3024717{col 49}{space 1}    3.14{col 58}{space 3}0.004{col 66}{space 4} .3296557{col 79}{space 3} 1.566904
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2}  .015674{col 38}{space 2} .0624368{col 49}{space 1}    0.25{col 58}{space 3}0.804{col 66}{space 4}-.1120236{col 79}{space 3} .1433716
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.822164{col 38}{space 2} .2714078{col 49}{space 1}   10.40{col 58}{space 3}0.000{col 66}{space 4} 2.267073{col 79}{space 3} 3.377255
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder##dvictimindex gender age education income unemployed i.religion arab kurd wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       898
                                                {txt}F(12, 29)         =  {res}   117.64
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1800
                                                {txt}Root MSE          =    {res} .66194

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2} -.358279{col 38}{space 2} .2360109{col 49}{space 1}   -1.52{col 58}{space 3}0.140{col 66}{space 4}-.8409755{col 79}{space 3} .1244175
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.2888463{col 38}{space 2} .2114288{col 49}{space 1}   -1.37{col 58}{space 3}0.182{col 66}{space 4}-.7212668{col 79}{space 3} .1435741
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .8439925{col 38}{space 2} .2614883{col 49}{space 1}    3.23{col 58}{space 3}0.003{col 66}{space 4}  .309189{col 79}{space 3} 1.378796
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .0033583{col 38}{space 2} .0338828{col 49}{space 1}    0.10{col 58}{space 3}0.922{col 66}{space 4}-.0659399{col 79}{space 3} .0726564
{txt}{space 21}age {c |}{col 26}{res}{space 2} -.001239{col 38}{space 2} .0027268{col 49}{space 1}   -0.45{col 58}{space 3}0.653{col 66}{space 4}-.0068159{col 79}{space 3} .0043378
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0963891{col 38}{space 2} .0680443{col 49}{space 1}    1.42{col 58}{space 3}0.167{col 66}{space 4}-.0427772{col 79}{space 3} .2355553
{txt}{space 18}income {c |}{col 26}{res}{space 2}-.0356495{col 38}{space 2} .0348981{col 49}{space 1}   -1.02{col 58}{space 3}0.315{col 66}{space 4}-.1070241{col 79}{space 3} .0357251
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0882609{col 38}{space 2} .0986008{col 49}{space 1}   -0.90{col 58}{space 3}0.378{col 66}{space 4}-.2899221{col 79}{space 3} .1134003
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .7667377{col 38}{space 2} .0302398{col 49}{space 1}   25.36{col 58}{space 3}0.000{col 66}{space 4} .7048904{col 79}{space 3} .8285849
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.1329526{col 38}{space 2} .0933818{col 49}{space 1}   -1.42{col 58}{space 3}0.165{col 66}{space 4}-.3239398{col 79}{space 3} .0580347
{txt}{space 20}kurd {c |}{col 26}{res}{space 2}-.2066497{col 38}{space 2}  .123808{col 49}{space 1}   -1.67{col 58}{space 3}0.106{col 66}{space 4}-.4598656{col 79}{space 3} .0465661
{txt}{space 20}wave {c |}{col 26}{res}{space 2}-.1305652{col 38}{space 2}  .090574{col 49}{space 1}   -1.44{col 58}{space 3}0.160{col 66}{space 4}-.3158099{col 79}{space 3} .0546794
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}  2.77544{col 38}{space 2} .3489662{col 49}{space 1}    7.95{col 58}{space 3}0.000{col 66}{space 4} 2.061724{col 79}{space 3} 3.489156
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder##dvictimindex gender age education income unemployed i.religion arab kurd wave,  vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       898
                                                {txt}F(12, 29)         =  {res}    25.55
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1946
                                                {txt}Root MSE          =    {res} .65597

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.6181125{col 38}{space 2} .2903336{col 49}{space 1}   -2.13{col 58}{space 3}0.042{col 66}{space 4}-1.211911{col 79}{space 3}-.0243136
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.5255972{col 38}{space 2} .2728203{col 49}{space 1}   -1.93{col 58}{space 3}0.064{col 66}{space 4}-1.083577{col 79}{space 3} .0323829
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}  .988296{col 38}{space 2} .3126956{col 49}{space 1}    3.16{col 58}{space 3}0.004{col 66}{space 4} .3487616{col 79}{space 3}  1.62783
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2}-.0066217{col 38}{space 2} .0209709{col 49}{space 1}   -0.32{col 58}{space 3}0.754{col 66}{space 4}-.0495121{col 79}{space 3} .0362687
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0027745{col 38}{space 2} .0031364{col 49}{space 1}   -0.88{col 58}{space 3}0.384{col 66}{space 4}-.0091891{col 79}{space 3} .0036401
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0924018{col 38}{space 2} .0584565{col 49}{space 1}    1.58{col 58}{space 3}0.125{col 66}{space 4}-.0271552{col 79}{space 3} .2119588
{txt}{space 18}income {c |}{col 26}{res}{space 2} -.046006{col 38}{space 2} .0324334{col 49}{space 1}   -1.42{col 58}{space 3}0.167{col 66}{space 4}-.1123398{col 79}{space 3} .0203277
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0392522{col 38}{space 2} .1164093{col 49}{space 1}   -0.34{col 58}{space 3}0.738{col 66}{space 4} -.277336{col 79}{space 3} .1988316
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .5664708{col 38}{space 2} .0415409{col 49}{space 1}   13.64{col 58}{space 3}0.000{col 66}{space 4} .4815102{col 79}{space 3} .6514315
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.0502477{col 38}{space 2} .1092915{col 49}{space 1}   -0.46{col 58}{space 3}0.649{col 66}{space 4} -.273774{col 79}{space 3} .1732786
{txt}{space 20}kurd {c |}{col 26}{res}{space 2}-.6142998{col 38}{space 2} .1710116{col 49}{space 1}   -3.59{col 58}{space 3}0.001{col 66}{space 4}-.9640578{col 79}{space 3}-.2645419
{txt}{space 20}wave {c |}{col 26}{res}{space 2} -.014115{col 38}{space 2} .0609339{col 49}{space 1}   -0.23{col 58}{space 3}0.818{col 66}{space 4}-.1387389{col 79}{space 3} .1105089
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.958973{col 38}{space 2} .4523519{col 49}{space 1}    6.54{col 58}{space 3}0.000{col 66}{space 4} 2.033809{col 79}{space 3} 3.884136
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Figure 4 (see "Figures 2-4 data.xls" for formatting details)
. 
. tab closegay if victimorder==0

{txt}How close do you {c |}
         feel to {c |}
  gay/homosexual {c |}
       people?   {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
not at all close {c |}{res}        281       62.44       62.44
{txt}               2 {c |}{res}         52       11.56       74.00
{txt}               3 {c |}{res}         22        4.89       78.89
{txt}               4 {c |}{res}         27        6.00       84.89
{txt}               5 {c |}{res}         28        6.22       91.11
{txt}               6 {c |}{res}         22        4.89       96.00
{txt}               7 {c |}{res}         11        2.44       98.44
{txt}               8 {c |}{res}          5        1.11       99.56
{txt}               9 {c |}{res}          2        0.44      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        450      100.00
{txt}
{com}. tab closegay if victimorder==1

{txt}How close do you {c |}
         feel to {c |}
  gay/homosexual {c |}
       people?   {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
not at all close {c |}{res}        251       55.78       55.78
{txt}               2 {c |}{res}         16        3.56       59.33
{txt}               3 {c |}{res}         73       16.22       75.56
{txt}               4 {c |}{res}         27        6.00       81.56
{txt}               5 {c |}{res}         31        6.89       88.44
{txt}               6 {c |}{res}         22        4.89       93.33
{txt}               7 {c |}{res}         14        3.11       96.44
{txt}               8 {c |}{res}         11        2.44       98.89
{txt}               9 {c |}{res}          5        1.11      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        450      100.00
{txt}
{com}. 
. *Appendix Figure 1 (see Coppock 2021Dataverse at https://doi.org/10.7910/DVN/VE6VSR for details. Here is the code for each figure). 
. 
. twoway scatter Y_jitter_protect Z_jitter_protect, mcolor(%20) msize(1) || scatter group_mean_protect victimorder, msize(2) mcolor(red) || rcap conf_high_protect conf_low_protect  victimorder, color(red)
{res}{txt}
{com}. 
. graph save g1.gph
{res}{txt}file {bf:g1.gph} saved

{com}. 
. twoway scatter Y_jitter_rights Z_jitter_rights, mcolor(%20) msize(1) || scatter group_mean_rights victimorder, msize(2) mcolor(red) || rcap conf_high_rights conf_low_rights  victimorder, color(red)
{res}{txt}
{com}. 
. graph save g2.gph
{res}{txt}file {bf:g2.gph} saved

{com}. 
. twoway scatter Y_jitter_close Z_jitter_close, mcolor(%20) msize(1) || scatter group_mean_close victimorder, msize(2) mcolor(red) || rcap conf_high_close conf_low_close  victimorder, color(red)
{res}{txt}
{com}. 
. graph save g3.gph
{res}{txt}file {bf:g3.gph} saved

{com}. 
. graph combine "g1.gph" "g2.gph" "g3.gph"
{res}{txt}
{com}. *Note additional formatting requires the "Appendix Figure 1 Formatting.grec" file with the command graph play "Appendix Figure 1 Formatting.grec" 
. 
. *Online Appendix Material
. 
. *Data Collection and Sampling Locations
. 
. *Table 1. Sampling Locations within Mosul
. 
. tab location

     {txt}Location of {c |}
       interview {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
    Al - Andalus {c |}{res}         10        1.11        1.11
{txt}      Al - Araby {c |}{res}         15        1.67        2.78
{txt}      Al - Baker {c |}{res}         10        1.11        3.89
{txt}    Al - Danadan {c |}{res}         10        1.11        5.00
{txt}     Al - Hadbaa {c |}{res}         91       10.11       15.11
{txt}     Al - Jameaa {c |}{res}         10        1.11       16.22
{txt}     Al - Jazaer {c |}{res}         13        1.44       17.67
{txt}    Al - Karamah {c |}{res}         14        1.56       19.22
{txt}   Al - Mansour  {c |}{res}         10        1.11       20.33
{txt}    Al - Masarif {c |}{res}          9        1.00       21.33
{txt}   Al - Ma’amoon {c |}{res}         75        8.33       29.67
{txt}      Al - Medan {c |}{res}         75        8.33       38.00
{txt}  Al - Muhandsin {c |}{res}         75        8.33       46.33
{txt}  Al - Muthannah {c |}{res}         10        1.11       47.44
{txt}     Al - Rifaee {c |}{res}         20        2.22       49.67
{txt}      Al - Sihha {c |}{res}         76        8.44       58.11
{txt}     Al - Sukkar {c |}{res}         10        1.11       59.22
{txt}   Al - Taameem  {c |}{res}         10        1.11       60.33
{txt}     Al - Tahrir {c |}{res}         89        9.89       70.22
{txt}    Al - Tayaran {c |}{res}         75        8.33       78.56
{txt}    Al - Yarmook {c |}{res}         84        9.33       87.89
{txt}     Al - Zahraa {c |}{res}         10        1.11       89.00
{txt}   Al - Zanjily  {c |}{res}         15        1.67       90.67
{txt}     Al - dawasa {c |}{res}         10        1.11       91.78
{txt}      Al - najar {c |}{res}         10        1.11       92.89
{txt}       Al -Rifaq {c |}{res}         10        1.11       94.00
{txt}    Al- Intisar  {c |}{res}         20        2.22       96.22
{txt}Mosul Al jadedah {c |}{res}         14        1.56       97.78
{txt}         Nablus  {c |}{res}         10        1.11       98.89
{txt}     Wadi Hajar  {c |}{res}         10        1.11      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Discussion of Results with respect to PAP
. 
. *Model 1 includes the results on "closeness to gay people" which is positive and significant. 
. 
. reg closegay victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(1, 898)         =  {res}     7.49
                                                {txt}Prob > F          = {res}    0.0063
                                                {txt}R-squared         = {res}    0.0083
                                                {txt}Root MSE          =    {res} 1.9733

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    closegay{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}      .36{col 26}{space 2} .1315546{col 37}{space 1}    2.74{col 46}{space 3}0.006{col 54}{space 4} .1018098{col 67}{space 3} .6181902
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.146667{col 26}{space 2} .0878211{col 37}{space 1}   24.44{col 46}{space 3}0.000{col 54}{space 4} 1.974308{col 67}{space 3} 2.319025
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Model 2 indicates that LGBT+ priming did not increase the willingness to have homosexuals as neighbors. 90% indicated they would not accept homosexuals as neighbors and 10% would. 
. 
. reg revgayneighbors victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(1, 898)         =  {res}     1.19
                                                {txt}Prob > F          = {res}    0.2763
                                                {txt}R-squared         = {res}    0.0013
                                                {txt}Root MSE          =    {res} .30597

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revgayneig~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.0222222{col 26}{space 2} .0203983{col 37}{space 1}   -1.09{col 46}{space 3}0.276{col 54}{space 4}-.0622561{col 67}{space 3} .0178117
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.115556{col 26}{space 2} .0150872{col 37}{space 1}   73.94{col 46}{space 3}0.000{col 54}{space 4} 1.085945{col 67}{space 3} 1.145166
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab revgayneighbors

  {txt}Would you {c |}
 be willing {c |}
    to have {c |}
homosexuals {c |}
         as {c |}
neighbors?  {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         no {c |}{res}        806       89.56       89.56
{txt}        yes {c |}{res}         94       10.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Model 3 reports the ATE for a dummy variable coded 1 for "Homosexuality should be accepted by society" and 0 if not. The treatment did not move opinion on this item. 90% believe homosexuality should not be generally acceptable to society and only 10% believe it should. 
. 
. reg revacceptsociety victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       897
                                                {txt}F(1, 895)         =  {res}     0.46
                                                {txt}Prob > F          = {res}    0.4984
                                                {txt}R-squared         = {res}    0.0005
                                                {txt}Root MSE          =    {res}  .3007

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revaccepts~y{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.0136017{col 26}{space 2} .0200818{col 37}{space 1}   -0.68{col 46}{space 3}0.498{col 54}{space 4}-.0530145{col 67}{space 3} .0258112
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.107143{col 26}{space 2} .0146291{col 37}{space 1}   75.68{col 46}{space 3}0.000{col 54}{space 4} 1.078431{col 67}{space 3} 1.135854
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab revacceptsociety

     {txt}homosexuality should/should not be {c |}
                    accepted by society {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
Homosexuality should not be accepted by {c |}{res}        807       89.97       89.97
{txt}Homosexuality should be accepted by soc {c |}{res}         90       10.03      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        897      100.00
{txt}
{com}. 
. *Similarly, Model 4 indicates that the treatment did not move opinions about personal acceptance of homosexuality. 68.4% indicate that they believe homosexuality is "never acceptable" and less than 1% indicated that it was "always acceptable" with the remainder distributed on the other response categories with a mean of 2.19, SD=2.18). 
. 
. reg acceptablegay victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(1, 898)         =  {res}     1.01
                                                {txt}Prob > F          = {res}    0.3140
                                                {txt}R-squared         = {res}    0.0011
                                                {txt}Root MSE          =    {res} 2.1839

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}acceptable~y{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}-.1466667{col 26}{space 2} .1455952{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.4324132{col 67}{space 3} .1390798
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.262222{col 26}{space 2}  .102168{col 37}{space 1}   22.14{col 46}{space 3}0.000{col 54}{space 4} 2.061706{col 67}{space 3} 2.462738
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab acceptablegay

     {txt}Do you {c |}
    believe {c |}
homosexuali {c |}
      ty is {c |}
 acceptable {c |}
         or {c |}
unacceptabl {c |}
        e?  {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      never {c |}{res}        616       68.44       68.44
{txt}          2 {c |}{res}         45        5.00       73.44
{txt}          3 {c |}{res}         72        8.00       81.44
{txt}          4 {c |}{res}         44        4.89       86.33
{txt}          5 {c |}{res}         24        2.67       89.00
{txt}          6 {c |}{res}         19        2.11       91.11
{txt}          7 {c |}{res}         35        3.89       95.00
{txt}          8 {c |}{res}         18        2.00       97.00
{txt}          9 {c |}{res}         21        2.33       99.33
{txt}     always {c |}{res}          6        0.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. sum acceptablegay

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
acceptable~y {c |}{res}        900    2.188889    2.183946          1         10
{txt}
{com}. 
. *Finally, Models 5 and 6 compare results to our main and alternative dependent variables – agreement that "Gay/homosexual people are entitled to human rights protections under Iraqi law" and that "Iraqi authorities should do more to protect gay/homosexual people from violence" which is discussed in the manuscript and in greater detail in the subsequent section of the appendix. 
. 
. reg revhumanrights victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(1, 898)         =  {res}    27.26
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0295
                                                {txt}Root MSE          =    {res} .71511

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .2488889{col 26}{space 2}  .047674{col 37}{space 1}    5.22{col 46}{space 3}0.000{col 54}{space 4} .1553234{col 67}{space 3} .3424544
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.468889{col 26}{space 2} .0399247{col 37}{space 1}   61.84{col 46}{space 3}0.000{col 54}{space 4} 2.390532{col 67}{space 3} 2.547246
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder,  vce(robust)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(1, 898)         =  {res}    50.08
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0528
                                                {txt}Root MSE          =    {res} .70653

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .3333333{col 26}{space 2} .0471017{col 37}{space 1}    7.08{col 46}{space 3}0.000{col 54}{space 4} .2408911{col 67}{space 3} .4257756
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.397778{col 26}{space 2} .0395344{col 37}{space 1}   60.65{col 46}{space 3}0.000{col 54}{space 4} 2.320187{col 67}{space 3} 2.475368
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *What we find most remarkable in the latter two variables on support for human rights and protections from violence is that unlike earlier items, where most respondents are quite conservative in their views, they are, despite this conservatism toward the acceptance of homosexuality, much more agreeable to the proposition that gay/homosexual people are entitled to human rights protections (8.4% strongly agree, 48.2% somewhat agree,  37.6% somewhat disagree, 5.8% strongly disagree), and that Iraqi authorities should do more to protect them from violence (8.6% strongly agree, 44.8% somewhat agree,  41.2% somewhat disagree, 5.4% strongly disagree), which we argue is important to a deliberative understanding of tolerance.
. 
. tab revhumanrights

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         52        5.78        5.78
{txt}somewhat disagree {c |}{res}        338       37.56       43.33
{txt}   somewhat agree {c |}{res}        434       48.22       91.56
{txt}   strongly agree {c |}{res}         76        8.44      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        900      100.00
{txt}
{com}. tab revprotectgay

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         49        5.44        5.44
{txt}somewhat disagree {c |}{res}        371       41.22       46.67
{txt}   somewhat agree {c |}{res}        403       44.78       91.44
{txt}   strongly agree {c |}{res}         77        8.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Victimization Experiences
. 
. sum punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fledhomeisis homelootedisis womenabusedisis

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
punishedisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}fampunishe~s {c |}{res}        900    .4755556    .4996798          0          1
{txt}{space 1}injuredisis {c |}{res}        900    .1322222    .3389205          0          1
{txt}faminjured~s {c |}{res}        900    .0833333    .2765391          0          1
{txt}famkilledi~s {c |}{res}        900    .2011111    .4010538          0          1
{txt}{hline 13}{c +}{hline 57}
imprisoned~s {c |}{res}        900    .3244444    .4684272          0          1
{txt}fledhomeisis {c |}{res}        900    .3922222    .4885173          0          1
{txt}homelooted~s {c |}{res}        900    .6577778    .4747174          0          1
{txt}womenabuse~s {c |}{res}        900    .5833333    .4932808          0          1
{txt}
{com}. 
. *gen dvictimindex = 1 if punishedisis==1 | fampunishedisis==1 | injuredisis==1 | faminjuredisis==1 | famkilledisis==1 | imprisonedisis==1 | fledhomeisis==1 | homelootedisis==1 | womenabusedisis==1
. 
. tab dvictimindex

{txt}0 = no isis {c |}
vicitmizati {c |}
on, 1 = any {c |}
       isis {c |}
victimizati {c |}
         on {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        144       16.00       16.00
{txt}          1 {c |}{res}        756       84.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        900      100.00
{txt}
{com}. 
. *Factor analysis
. 
. factor punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fledhomeisis homelootedisis womenabusedisis
{txt}(obs=900)

Factor analysis/correlation{col 50}Number of obs    = {res}       900
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       6
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      36

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.67322      1.63204            0.5910       0.5910
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.04118      0.41885            0.2302       0.8212
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.62233      0.17948            0.1376       0.9587
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.44285      0.20754            0.0979       1.0566
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.23531      0.15242            0.0520       1.1087
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.08288      0.17180            0.0183       1.1270
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.08891      0.13242           -0.0197       1.1073
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.22133      0.04285           -0.0489       1.0584
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.26418            .           -0.0584       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}36{txt}) ={res} 3515.88{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{space 1}{ralign 8:Factor5}{space 1}{space 1}{ralign 8:Factor6}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:punishedisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8488}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0205}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2206}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3084}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1361}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0245}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1162}}}{space 1}
{space 4}{space 0}{ralign 12:fampunishe~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9214}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2605}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0388}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0668}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0129}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0199}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0767}}}{space 1}
{space 4}{space 0}{ralign 12:injuredisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1959}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6418}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2304}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1686}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0119}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0359}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4668}}}{space 1}
{space 4}{space 0}{ralign 12:faminjured~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0968}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5007}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2051}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2977}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2377}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0275}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5520}}}{space 1}
{space 4}{space 0}{ralign 12:famkilledi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1102}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1763}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1306}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0259}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2586}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1961}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8337}}}{space 1}
{space 4}{space 0}{ralign 12:imprisoned~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2755}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2130}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3171}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1984}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2355}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1364}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6648}}}{space 1}
{space 4}{space 0}{ralign 12:fledhomeisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0433}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3543}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4548}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2717}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1336}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0573}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5707}}}{space 1}
{space 4}{space 0}{ralign 12:homelooted~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6518}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3145}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3777}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1620}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0601}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0885}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2959}}}{space 1}
{space 4}{space 0}{ralign 12:womenabuse~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7357}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0965}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0992}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2936}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1271}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1082}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3255}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *also consider tetrachoric correlation analysis
. 
. tetrachoric  punishedisis fampunishedisis injuredisis faminjuredisis famkilledisis imprisonedisis fledhomeisis homelootedisis womenabusedisis, posdef
{res}{txt}(obs=900)

matrix with tetrachoric correlations is not positive semidefinite;
  it has {res}3{txt} negative eigenvalues
  maxdiff(corr,adj-corr) = {res} 0.3742
{txt}  (adj-corr: tetrachoric correlations adjusted to be positive semidefinite)

{ralign 12:adj-corr} {c |} punish~s fampun~s injure~s faminj~s famkil~s impris~s fledho~s homelo~s
{hline 13}{c +}{hline 72}
punishedisis {c |} {res}  1.0000 
{txt}fampunishe~s {c |} {res}  0.7588   1.0000 
 {txt}injuredisis {c |} {res}  0.1660   0.5725   1.0000 
{txt}faminjured~s {c |} {res} -0.6258   0.0116   0.4055   1.0000 
{txt}famkilledi~s {c |} {res} -0.1607  -0.2522  -0.4122   0.0639   1.0000 
{txt}imprisoned~s {c |} {res}  0.3344   0.2512  -0.4819  -0.1530   0.0009   1.0000 
{txt}fledhomeisis {c |} {res} -0.0004  -0.1897  -0.1650  -0.2927   0.2859  -0.1885   1.0000 
{txt}homelooted~s {c |} {res}  0.5486   0.6293   0.0151  -0.2128  -0.0367   0.3051   0.3960   1.0000 
{txt}womenabuse~s {c |} {res}  0.6345   0.7778   0.1105  -0.1204  -0.0387   0.3094  -0.1573   0.8107 

{txt}{ralign 12:adj-corr} {c |} womena~s
{hline 13}{c +}{hline 9}
womenabuse~s {c |} {res}  1.0000 
{txt}
{com}. 
. *Table 2. Correlates of Victimization (OLS Regression)
. 
. reg mmx_factorvictimindex gender age education income unemployed i.religion arab, robust

{txt}Linear regression                               Number of obs     = {res}       898
                                                {txt}F(7, 890)         =  {res}    28.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1415
                                                {txt}Root MSE          =    {res} .36439

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}mmx_factor~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2} .2462405{col 26}{space 2} .0268657{col 37}{space 1}    9.17{col 46}{space 3}0.000{col 54}{space 4}  .193513{col 67}{space 3} .2989681
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0094544{col 26}{space 2}  .001713{col 37}{space 1}   -5.52{col 46}{space 3}0.000{col 54}{space 4}-.0128164{col 67}{space 3}-.0060924
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0028384{col 26}{space 2} .0162422{col 37}{space 1}    0.17{col 46}{space 3}0.861{col 54}{space 4}-.0290391{col 67}{space 3} .0347158
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0208852{col 26}{space 2}  .014513{col 37}{space 1}   -1.44{col 46}{space 3}0.150{col 54}{space 4}-.0493689{col 67}{space 3} .0075985
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} .0243098{col 26}{space 2} .0747657{col 37}{space 1}    0.33{col 46}{space 3}0.745{col 54}{space 4}-.1224278{col 67}{space 3} .1710475
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 7}Shia  {c |}{col 14}{res}{space 2} .3501731{col 26}{space 2} .0507594{col 37}{space 1}    6.90{col 46}{space 3}0.000{col 54}{space 4} .2505511{col 67}{space 3} .4497951
{txt}{space 8}arab {c |}{col 14}{res}{space 2}-.0526366{col 26}{space 2} .0332481{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.1178904{col 67}{space 3} .0126172
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8068619{col 26}{space 2} .0927939{col 37}{space 1}    8.70{col 46}{space 3}0.000{col 54}{space 4} .6247416{col 67}{space 3} .9889823
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *LGBT+ Victimization Treatment Randomization and Balance Tests
. 
. *Table 3. Demographic Balance across Treatment and Control Groups
. 
. sum gender age education income employment religion ethnicity dvictimindex if victimorder==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        450    .2222222    .4162024          0          1
{txt}{space 9}age {c |}{res}        448    27.26116    6.173561         18         52
{txt}{space 3}education {c |}{res}        450    3.933333    .3058451          2          4
{txt}{space 6}income {c |}{res}        450    3.037778    .4230491          1          4
{txt}{space 2}employment {c |}{res}        450        5.82    3.073132          1          9
{txt}{hline 13}{c +}{hline 57}
{space 4}religion {c |}{res}        450    1.004444    .0665924          1          2
{txt}{space 3}ethnicity {c |}{res}        450    1.246667    .6360023          1          4
{txt}dvictimindex {c |}{res}        450    .7622222    .4261961          0          1
{txt}
{com}. sum gender age education income employment religion ethnicity dvictimindex if victimorder==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}        450    .2222222    .4162024          0          1
{txt}{space 9}age {c |}{res}        450    27.57556    7.731279         18         60
{txt}{space 3}education {c |}{res}        450    3.373333    .7510646          1          4
{txt}{space 6}income {c |}{res}        450    3.193333    .8470833          1          4
{txt}{space 2}employment {c |}{res}        450    5.773333    2.725316          1          9
{txt}{hline 13}{c +}{hline 57}
{space 4}religion {c |}{res}        450    1.093333    .2912228          1          2
{txt}{space 3}ethnicity {c |}{res}        450    1.137778    .3577266          1          3
{txt}dvictimindex {c |}{res}        450    .9177778    .2750087          0          1
{txt}
{com}. 
. *Two-sample T-tests, Kolmogorov-Smirnov tests for equality of distribution functions
. 
. iebaltab gender age education income employment sunni arab dvictimindex, groupvar(victimorder) savexlsx(treatment balance)

{res}{phang}Balance table saved in Excel format to: {browse "treatment balance.xlsx":treatment balance.xlsx}{p_end}
{txt}
{com}. 
. ksmirnov gender, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0000       1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 900 observations.

{com}. ksmirnov age, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.1439       0.000
{txt}1                  {res} -0.0812       0.052
{txt}Combined K-S       {res}  0.1439       0.000

{txt}Note: Ties exist in combined dataset;
      there are 36 unique values out of 898 observations.

{com}. ksmirnov education, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.4200       0.000
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.4200       0.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 900 observations.

{com}. ksmirnov income, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0711       0.103
{txt}1                  {res} -0.2956       0.000
{txt}Combined K-S       {res}  0.2956       0.000

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 900 observations.

{com}. ksmirnov employment, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0889       0.029
{txt}1                  {res} -0.0933       0.020
{txt}Combined K-S       {res}  0.0933       0.040

{txt}Note: Ties exist in combined dataset;
      there are 9 unique values out of 900 observations.

{com}. ksmirnov sunni, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0889       0.029
{txt}1                  {res}  0.0000       1.000
{txt}Combined K-S       {res}  0.0889       0.057

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 900 observations.

{com}. ksmirnov arab, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.0267       0.726
{txt}Combined K-S       {res}  0.0267       0.997

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 900 observations.

{com}. ksmirnov dvictimindex, by(victimorder)

{txt}Two-sample Kolmogorov–Smirnov test for equality of distribution functions

Smaller group             D     p-value  
{hline 39}
0                  {res}  0.0000       1.000
{txt}1                  {res} -0.1556       0.000
{txt}Combined K-S       {res}  0.1556       0.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 900 observations.

{com}. 
. *Table 4. Balance Across Treatment/Control (Logit Regression)
. 
. logit victimorder dvictimindex gender age education income unemployed ib2.religion arab, cluster(locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-622.44394}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-480.98932}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-472.58247}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-472.40857}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-472.40841}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-472.40841}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:898}
{txt}{col 57}{lalign 13:Wald chi2({res:8})}{col 70} = {res}{ralign 6:73.65}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-472.40841}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2410}

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} victimorder{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
dvictimindex {c |}{col 14}{res}{space 2}-.8581425{col 26}{space 2} .4641605{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4} -1.76788{col 67}{space 3} .0515954
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .1935287{col 26}{space 2} .6811548{col 37}{space 1}    0.28{col 46}{space 3}0.776{col 54}{space 4} -1.14151{col 67}{space 3} 1.528568
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0030235{col 26}{space 2}  .022229{col 37}{space 1}    0.14{col 46}{space 3}0.892{col 54}{space 4}-.0405446{col 67}{space 3} .0465916
{txt}{space 3}education {c |}{col 14}{res}{space 2} 2.426608{col 26}{space 2} .4466342{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} 1.551221{col 67}{space 3} 3.301995
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.7640868{col 26}{space 2}  .193646{col 37}{space 1}   -3.95{col 46}{space 3}0.000{col 54}{space 4}-1.143626{col 67}{space 3}-.3845477
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2} 1.162763{col 26}{space 2} 1.052915{col 37}{space 1}    1.10{col 46}{space 3}0.269{col 54}{space 4}-.9009116{col 67}{space 3} 3.226438
{txt}{space 12} {c |}
{space 4}religion {c |}
{space 6}Sunni  {c |}{col 14}{res}{space 2} 2.903512{col 26}{space 2} 1.108849{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .7302081{col 67}{space 3} 5.076816
{txt}{space 12} {c |}
{space 8}arab {c |}{col 14}{res}{space 2}-.2896016{col 26}{space 2} .2929885{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4}-.8638486{col 67}{space 3} .2846454
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-8.748871{col 26}{space 2} 2.276517{col 37}{space 1}   -3.84{col 46}{space 3}0.000{col 54}{space 4}-13.21076{col 67}{space 3}-4.286979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Table 5. Treatment Effect Estimation Adjusted for Imbalances
. 
. teffects ipw (revprotectgays)  (victimorder dvictimindex education income i.religion ), vce(cluster locale)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.263e-23}  
Iteration 1:{space 2}EE criterion = {res: 1.438e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       900
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}victimorder {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .3226749{col 26}{space 2} .0873594{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4} .1514537{col 67}{space 3}  .493896
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}victimorder {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.425159{col 26}{space 2} .0729545{col 37}{space 1}   33.24{col 46}{space 3}0.000{col 54}{space 4}  2.28217{col 67}{space 3} 2.568147
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (revprotectgays dvictimindex  education income i.religion ) (victimorder dvictimindex education income i.religion ), vce(cluster locale)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.263e-23}  
Iteration 1:{space 2}EE criterion = {res: 4.709e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       900
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}victimorder {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .3331568{col 26}{space 2} .1022794{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .1326929{col 67}{space 3} .5336208
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}victimorder {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.421591{col 26}{space 2} .0702292{col 37}{space 1}   34.48{col 46}{space 3}0.000{col 54}{space 4} 2.283944{col 67}{space 3} 2.559238
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (revprotectgays dvictimindex education income i.religion ) (victimorder dvictimindex education income i.religion ), vce(cluster locale)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.263e-23}  
Iteration 1:{space 2}EE criterion = {res: 2.379e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       900
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}victimorder {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .3361326{col 26}{space 2} .1028306{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .1345883{col 67}{space 3}  .537677
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}victimorder {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.427288{col 26}{space 2} .0710953{col 37}{space 1}   34.14{col 46}{space 3}0.000{col 54}{space 4} 2.287943{col 67}{space 3} 2.566632
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (revprotectgays) (victimorder dvictimindex education income i.religion , logit), vce(iid)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       900
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}        291
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}victimorder {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .2863751{col 26}{space 2} .0945678{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .1010256{col 67}{space 3} .4717247
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Sensitivity Analysis
. 
. reg revprotectgay victimorder wave, vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    11.53
                                                {txt}Prob > F          = {res}    0.0002
                                                {txt}R-squared         = {res}    0.0578
                                                {txt}Root MSE          =    {res} .70507

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .3333333{col 26}{space 2} .0732539{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .1835123{col 67}{space 3} .4831544
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.1083333{col 26}{space 2} .0899857{col 37}{space 1}   -1.20{col 46}{space 3}0.238{col 54}{space 4}-.2923747{col 67}{space 3} .0757081
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.578333{col 26}{space 2} .1855639{col 37}{space 1}   13.89{col 46}{space 3}0.000{col 54}{space 4} 2.198813{col 67}{space 3} 2.957854
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder dvictimindex gender age education income i.employment i.religion arab kurd wave, vce(cluster locale)

{txt}Linear regression                               Number of obs     = {res}       898
                                                {txt}F(18, 29)         =  {res}   842.69
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1648
                                                {txt}Root MSE          =    {res} .67032

{txt}{ralign 87:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       revprotectgays{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}victimorder {c |}{col 23}{res}{space 2}  .338612{col 35}{space 2} .1046573{col 46}{space 1}    3.24{col 55}{space 3}0.003{col 63}{space 4} .1245638{col 76}{space 3} .5526602
{txt}{space 9}dvictimindex {c |}{col 23}{res}{space 2} .2973227{col 35}{space 2} .1119154{col 46}{space 1}    2.66{col 55}{space 3}0.013{col 63}{space 4} .0684299{col 76}{space 3} .5262155
{txt}{space 15}gender {c |}{col 23}{res}{space 2} .0330199{col 35}{space 2} .0392695{col 46}{space 1}    0.84{col 55}{space 3}0.407{col 63}{space 4}-.0472953{col 76}{space 3} .1133351
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0025668{col 35}{space 2} .0027543{col 46}{space 1}   -0.93{col 55}{space 3}0.359{col 63}{space 4}   -.0082{col 76}{space 3} .0030665
{txt}{space 12}education {c |}{col 23}{res}{space 2} .0962648{col 35}{space 2} .0649861{col 46}{space 1}    1.48{col 55}{space 3}0.149{col 63}{space 4}-.0366466{col 76}{space 3} .2291763
{txt}{space 15}income {c |}{col 23}{res}{space 2}-.0516849{col 35}{space 2} .0326646{col 46}{space 1}   -1.58{col 55}{space 3}0.124{col 63}{space 4}-.1184915{col 76}{space 3} .0151218
{txt}{space 21} {c |}
{space 11}employment {c |}
{space 1}professional worker  {c |}{col 23}{res}{space 2}  .129636{col 35}{space 2} .0900941{col 46}{space 1}    1.44{col 55}{space 3}0.161{col 63}{space 4}-.0546272{col 76}{space 3} .3138992
{txt}{space 7}office worker  {c |}{col 23}{res}{space 2} .0975911{col 35}{space 2} .1073146{col 46}{space 1}    0.91{col 55}{space 3}0.371{col 63}{space 4}-.1218919{col 76}{space 3} .3170742
{txt}{space 7}manual worker  {c |}{col 23}{res}{space 2} -.100128{col 35}{space 2} .0628932{col 46}{space 1}   -1.59{col 55}{space 3}0.122{col 63}{space 4}-.2287592{col 76}{space 3} .0285031
{txt}farmer: has own farm  {c |}{col 23}{res}{space 2}-.2152243{col 35}{space 2}  .143558{col 46}{space 1}   -1.50{col 55}{space 3}0.145{col 63}{space 4}-.5088334{col 76}{space 3} .0783849
{txt}{space 1}agricultural worker  {c |}{col 23}{res}{space 2}-.1070216{col 35}{space 2}  .098796{col 46}{space 1}   -1.08{col 55}{space 3}0.288{col 63}{space 4}-.3090822{col 76}{space 3}  .095039
{txt}{space 8}armed forces  {c |}{col 23}{res}{space 2}-.2492191{col 35}{space 2} .1434227{col 46}{space 1}   -1.74{col 55}{space 3}0.093{col 63}{space 4}-.5425515{col 76}{space 3} .0441133
{txt}{space 10}unemployed  {c |}{col 23}{res}{space 2} -.059182{col 35}{space 2} .1275949{col 46}{space 1}   -0.46{col 55}{space 3}0.646{col 63}{space 4}-.3201429{col 76}{space 3} .2017788
{txt}{space 13}student  {c |}{col 23}{res}{space 2}  .071349{col 35}{space 2} .0590121{col 46}{space 1}    1.21{col 55}{space 3}0.236{col 63}{space 4}-.0493443{col 76}{space 3} .1920424
{txt}{space 21} {c |}
{space 13}religion {c |}
{space 16}Shia  {c |}{col 23}{res}{space 2} .7497129{col 35}{space 2} .0268182{col 46}{space 1}   27.96{col 55}{space 3}0.000{col 63}{space 4} .6948635{col 76}{space 3} .8045624
{txt}{space 17}arab {c |}{col 23}{res}{space 2}-.1555069{col 35}{space 2} .0877601{col 46}{space 1}   -1.77{col 55}{space 3}0.087{col 63}{space 4}-.3349964{col 76}{space 3} .0239826
{txt}{space 17}kurd {c |}{col 23}{res}{space 2}-.2104688{col 35}{space 2} .1265178{col 46}{space 1}   -1.66{col 55}{space 3}0.107{col 63}{space 4}-.4692268{col 76}{space 3} .0482891
{txt}{space 17}wave {c |}{col 23}{res}{space 2}-.1891123{col 35}{space 2} .0918462{col 46}{space 1}   -2.06{col 55}{space 3}0.049{col 63}{space 4}-.3769588{col 76}{space 3}-.0012658
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 2.438127{col 35}{space 2} .2978232{col 46}{space 1}    8.19{col 55}{space 3}0.000{col 63}{space 4}  1.82901{col 76}{space 3} 3.047244
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. regsensitivity bounds revprotectgays victimorder dvictimindex gender age education income employment arab religion, dmp vce(cluster locale)
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=         898
{col 48}{txt}Beta(short){col 67}{res}=       0.332
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=       0.346
{txt}Outcome{col 18}{res}: revprotectgays{col 48}{txt}R2(short){col 67}{res}=       0.052
{col 48}{txt}R2(medium){col 67}{res}=       0.134
{col 48}{txt}Var(Y){col 67}{res}=       0.527
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.187

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown point{col 67}{res}=        35.1%
{txt}Other Params{col 18}{res}: cbar = 1, rybar = +inf

{txt}{hline 80}
 rxbar{col 35} Beta
{hline 80}
{res}{col 2}0.000{col 35}{txt}[{res} 0.3462{txt}, {res} 0.3462{txt} ]
{col 2}{res}0.088{col 35}{txt}[{res} 0.2660{txt}, {res} 0.4265{txt} ]
{col 2}{res}0.177{col 35}{txt}[{res} 0.1831{txt}, {res} 0.5094{txt} ]
{col 2}{res}0.265{col 35}{txt}[{res} 0.0946{txt}, {res} 0.5979{txt} ]
{col 2}{res}0.354{col 35}{txt}[{res}-0.0037{txt}, {res} 0.6962{txt} ]
{col 2}{res}0.442{col 35}{txt}[{res}-0.1182{txt}, {res} 0.8107{txt} ]
{col 2}{res}0.531{col 35}{txt}[{res}-0.2602{txt}, {res} 0.9527{txt} ]
{col 2}{res}0.619{col 35}{txt}[{res}-0.4536{txt}, {res} 1.1461{txt} ]
{col 2}{res}0.708{col 35}{txt}[{res}-0.7629{txt}, {res} 1.4554{txt} ]
{col 2}{res}0.796{col 35}{txt}[{res}-1.4836{txt}, {res} 2.1761{txt} ]
{col 2}{res}0.866{col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}

{com}. regsensitivity plot
{txt}
{com}. regsensitivity bounds revprotectgays victimorder dvictimindex gender age education income employment arab religion, oster vce(cluster locale)
{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=         898
{col 48}{txt}Beta(short){col 67}{res}=       0.332
{txt}Treatment{col 18}{res}: victimorder{col 48}{txt}Beta(medium){col 67}{res}=       0.346
{txt}Outcome{col 18}{res}: revprotectgays{col 48}{txt}R2(short){col 67}{res}=       0.052
{col 48}{txt}R2(medium){col 67}{res}=       0.134
{col 48}{txt}Var(Y){col 67}{res}=       0.527
{col 48}{txt}Var(X){col 67}{res}=       0.250
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.187

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown point{col 67}{res}=          36%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
 Delta{col 35} Beta
{hline 80}
{res}{col 2}-0.990{col 35}{txt}{{res}  0.30{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.800{col 35}{txt}{{res}  0.30{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.600{col 35}{txt}{{res}  0.31{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.400{col 35}{txt}{{res}  0.31{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}-0.200{col 35}{txt}{{res}  0.32{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.000{col 35}{txt}{{res}  0.35{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.200{col 35}{txt}{{res}  0.47{txt}, {res}     .{txt}, {res}     .{txt} }
{col 2}{res}0.400{col 35}{txt}{{res} -0.31{txt}, {res}  0.15{txt}, {res}  1.05{txt} }
{col 2}{res}0.600{col 35}{txt}{{res} -1.09{txt}, {res}  0.24{txt}, {res}  1.69{txt} }
{col 2}{res}0.800{col 35}{txt}{{res} -2.30{txt}, {res}  0.26{txt}, {res}  2.74{txt} }
{col 2}{res}0.990{col 35}{txt}{{res}-15.89{txt}, {res}  0.27{txt}, {res} 10.98{txt} }
{hline 80}

{com}. regsensitivity plot
{txt}
{com}. 
. *Power Calculations
. 
. *Effect Size Estimations using One-War ANOVA 
. 
. power oneway, n(900) ngroups(2) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 12:N_per_group}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.09349}{res}{ralign 8:2}{res}{ralign 8:.00874}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.1082}{res}{ralign 8:2}{res}{ralign 8:.0117}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.1203}{res}{ralign 8:2}{res}{ralign 8:.01447}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.143}{res}{ralign 8:2}{res}{ralign 8:.02046}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. power oneway, n(300) ngroups(2) power(0.80 0.90 0.95 0.99)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 12:N_per_group}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:300}{res}{ralign 12:150}{res}{ralign 8:.1623}{res}{ralign 8:2}{res}{ralign 8:.02633}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:300}{res}{ralign 12:150}{res}{ralign 8:.1878}{res}{ralign 8:2}{res}{ralign 8:.03525}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:300}{res}{ralign 12:150}{res}{ralign 8:.2088}{res}{ralign 8:2}{res}{ralign 8:.0436}{res}{ralign 8:1}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:300}{res}{ralign 12:150}{res}{ralign 8:.2483}{res}{ralign 8:2}{res}{ralign 8:.06164}{res}{ralign 8:1}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. *See also
. oneway revprotectgay victimorder, tabulate

   {txt}1 = ISIS {c |}
victimizati {c |} Summary of Iraqi authorities should
  on before {c |}  do more to protect gay/homosexual
other LGBT+ {c |}            people from v
  0 = after {c |}        Mean   Std. dev.       Freq.
{hline 12}{c +}{hline 36}
          0 {c |}  {res} 2.3977778   .83865118         450
  {txt}        1 {c |}  {res} 2.7311111   .54315839         450
{txt}{hline 12}{c +}{hline 36}
      Total {c |}  {res} 2.5644444   .72555623         900

                        {txt}Analysis of variance
    Source              SS         df      MS            F     Prob > F
{hline 72}
Between groups     {res}         25      1           25     50.08     0.0000
{txt} Within groups     {res} 448.262222    898   .499178421
{txt}{hline 72}
    Total          {res} 473.262222    899   .526431838

{txt}Bartlett's equal-variances test: chi2({res}1{txt}) = {res} 82.0963{txt}    Prob>chi2 = {res}0.000
{txt}
{com}. power oneway, n(900) ngroups(2) power(0.80 0.90 0.95 0.99) varerror(0.50)
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated{txt} between-group variance{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 12:N_per_group}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:N_g}{txt}{txt}{ralign 8:Var_m}{txt}{txt}{ralign 8:Var_e}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.09349}{res}{ralign 8:2}{res}{ralign 8:.00437}{res}{ralign 8:.5}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.1082}{res}{ralign 8:2}{res}{ralign 8:.00585}{res}{ralign 8:.5}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.1203}{res}{ralign 8:2}{res}{ralign 8:.00723}{res}{ralign 8:.5}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:900}{res}{ralign 12:450}{res}{ralign 8:.143}{res}{ralign 8:2}{res}{ralign 8:.01023}{res}{ralign 8:.5}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 12}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. *Power Analysis for a Two-sample Means Test (Sample Size Estimation)
. 
. sum revprotectgay if victimorder==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revprotect~s {c |}{res}        450    2.397778    .8386512          1          4
{txt}
{com}. sum revprotectgay if victimorder==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revprotect~s {c |}{res}        450    2.731111    .5431584          1          4
{txt}
{com}. power twomeans  2.39778 2.73111, sd1(0.839) sd2(0.543) power(0.8 0.9 0.95 0.99) onesided table
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated sample sizes for a two-sample means test{p_end}{txt}Satterthwaite's t test assuming unequal variances
{txt}{txt}{bind:H0: m2 = m1}  {txt}versus  {bind:Ha: m2 > m1}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:m1}{txt}{txt}{ralign 8:m2}{txt}{txt}{ralign 8:sd1}{txt}{txt}{ralign 8:sd2}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:114}{res}{ralign 8:57}{res}{ralign 8:57}{res}{ralign 8:.3333}{res}{ralign 8:2.398}{res}{ralign 8:2.731}{res}{ralign 8:.839}{res}{ralign 8:.543}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:156}{res}{ralign 8:78}{res}{ralign 8:78}{res}{ralign 8:.3333}{res}{ralign 8:2.398}{res}{ralign 8:2.731}{res}{ralign 8:.839}{res}{ralign 8:.543}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:198}{res}{ralign 8:99}{res}{ralign 8:99}{res}{ralign 8:.3333}{res}{ralign 8:2.398}{res}{ralign 8:2.731}{res}{ralign 8:.839}{res}{ralign 8:.543}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:286}{res}{ralign 8:143}{res}{ralign 8:143}{res}{ralign 8:.3333}{res}{ralign 8:2.398}{res}{ralign 8:2.731}{res}{ralign 8:.839}{res}{ralign 8:.543}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. sum revprotectgay if dsharedsuffering==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revprotect~s {c |}{res}        343    2.865889    .4518672          1          4
{txt}
{com}. power twomeans  2.39778 2.8658, sd1(0.839) sd2(0.4519) power(0.8 0.9 0.95 0.99) onesided table
{res}
{txt}Performing iteration ...
{res}
{p 0 2 2}{txt}Estimated sample sizes for a two-sample means test{p_end}{txt}Satterthwaite's t test assuming unequal variances
{txt}{txt}{bind:H0: m2 = m1}  {txt}versus  {bind:Ha: m2 > m1}

  {txt}{c TLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c TRC}
  {txt}{c |}{txt}{txt}{ralign 8:alpha}{txt}{txt}{ralign 8:power}{txt}{txt}{ralign 8:N}{txt}{txt}{ralign 8:N1}{txt}{txt}{ralign 8:N2}{txt}{txt}{ralign 8:delta}{txt}{txt}{ralign 8:m1}{txt}{txt}{ralign 8:m2}{txt}{txt}{ralign 8:sd1}{txt}{txt}{ralign 8:sd2}{txt}{txt} {c |}
  {txt}{c LT}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c RT}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.8}{res}{ralign 8:54}{res}{ralign 8:27}{res}{ralign 8:27}{res}{ralign 8:.468}{res}{ralign 8:2.398}{res}{ralign 8:2.866}{res}{ralign 8:.839}{res}{ralign 8:.4519}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.9}{res}{ralign 8:74}{res}{ralign 8:37}{res}{ralign 8:37}{res}{ralign 8:.468}{res}{ralign 8:2.398}{res}{ralign 8:2.866}{res}{ralign 8:.839}{res}{ralign 8:.4519}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.95}{res}{ralign 8:92}{res}{ralign 8:46}{res}{ralign 8:46}{res}{ralign 8:.468}{res}{ralign 8:2.398}{res}{ralign 8:2.866}{res}{ralign 8:.839}{res}{ralign 8:.4519}{txt} {c |}
  {txt}{c |}{res}{ralign 8:.05}{res}{ralign 8:.99}{res}{ralign 8:134}{res}{ralign 8:67}{res}{ralign 8:67}{res}{ralign 8:.468}{res}{ralign 8:2.398}{res}{ralign 8:2.866}{res}{ralign 8:.839}{res}{ralign 8:.4519}{txt} {c |}
  {txt}{c BLC}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 8}{txt}{txt}{hline 1}{c BRC}

{com}. 
. *Manuscript Table 3 Robustness Checks 
. 
. *Table 6. Support for LGBT+ Protections (Ordered Probit Regression)
. 
. oprobit revprotectgay victimorder wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-984.50068}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-958.28739}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-958.25786}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-958.25786}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:19.44}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-958.25786}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0267}

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revprotectgays{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .5149211{col 28}{space 2}  .123591{col 39}{space 1}    4.17{col 48}{space 3}0.000{col 56}{space 4} .2726872{col 69}{space 3}  .757155
{txt}{space 10}wave {c |}{col 16}{res}{space 2}-.1721547{col 28}{space 2} .1389397{col 39}{space 1}   -1.24{col 48}{space 3}0.215{col 56}{space 4}-.4444716{col 69}{space 3} .1001621
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-1.713219{col 28}{space 2} .2803907{col 56}{space 4}-2.262775{col 69}{space 3}-1.163663
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-.1017287{col 28}{space 2} .2828554{col 56}{space 4}-.6561151{col 69}{space 3} .4526578
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.371591{col 28}{space 2}  .307266{col 56}{space 4} .7693608{col 69}{space 3} 1.973821
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revprotectgay victimorder dvictimindex wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-984.50068}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -945.0132}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-944.95668}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-944.95668}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:17.04}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0007}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-944.95668}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0402}

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revprotectgays{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .6060331{col 28}{space 2} .1555933{col 39}{space 1}    3.89{col 48}{space 3}0.000{col 56}{space 4} .3010759{col 69}{space 3} .9109904
{txt}{space 2}dvictimindex {c |}{col 16}{res}{space 2} .5479676{col 28}{space 2}   .20414{col 39}{space 1}    2.68{col 48}{space 3}0.007{col 56}{space 4} .1478607{col 69}{space 3} .9480746
{txt}{space 10}wave {c |}{col 16}{res}{space 2}-.2741488{col 28}{space 2} .1566594{col 39}{space 1}   -1.75{col 48}{space 3}0.080{col 56}{space 4}-.5811957{col 69}{space 3}  .032898
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-1.403886{col 28}{space 2} .3103014{col 56}{space 4}-2.012066{col 69}{space 3}-.7957069
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .2320932{col 28}{space 2} .3211769{col 56}{space 4} -.397402{col 69}{space 3} .8615884
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.732448{col 28}{space 2} .3684945{col 56}{space 4} 1.010212{col 69}{space 3} 2.454684
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revprotectgay victimorder##dvictimindex wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-984.50068}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-928.95711}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-928.82231}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-928.82229}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:4})}{col 70} = {res}{ralign 6:23.19}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-928.82229}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0566}

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      z{col 58}   P>|z|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.5367122{col 38}{space 2} .3674685{col 49}{space 1}   -1.46{col 58}{space 3}0.144{col 66}{space 4}-1.256937{col 79}{space 3} .1835128
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.3986211{col 38}{space 2} .3294229{col 49}{space 1}   -1.21{col 58}{space 3}0.226{col 66}{space 4}-1.044278{col 79}{space 3} .2470358
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} 1.318906{col 38}{space 2} .4266232{col 49}{space 1}    3.09{col 58}{space 3}0.002{col 66}{space 4} .4827397{col 79}{space 3} 2.155072
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2}-.1823139{col 38}{space 2} .1549824{col 49}{space 1}   -1.18{col 58}{space 3}0.239{col 66}{space 4}-.4860739{col 79}{space 3} .1214461
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-2.153086{col 38}{space 2} .4057065{col 66}{space 4}-2.948256{col 79}{space 3}-1.357916
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} -.478379{col 38}{space 2} .3914476{col 66}{space 4}-1.245602{col 79}{space 3} .2888442
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2}  1.05055{col 38}{space 2} .4051812{col 66}{space 4} .2564095{col 79}{space 3} 1.844691
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revprotectgay victimorder##dvictimindex gender age education income unemployed  i.religion arab kurd wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-982.89101}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -897.3694}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-896.98092}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-896.98074}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-896.98074}  
{res}
{txt}{col 1}Ordered probit regression{col 56}{lalign 13:Number of obs}{col 69} = {res}{ralign 7:898}
{txt}{col 56}{lalign 13:Wald chi2({res:12})}{col 69} = {res}{ralign 7:1104.77}
{txt}{col 56}{lalign 13:Prob > chi2}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-896.98074}{txt}{col 56}{lalign 13:Pseudo R2}{col 69} = {res}{ralign 7:0.0874}

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      z{col 58}   P>|z|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.5790085{col 38}{space 2} .3924136{col 49}{space 1}   -1.48{col 58}{space 3}0.140{col 66}{space 4}-1.348125{col 79}{space 3}  .190108
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.4711496{col 38}{space 2} .3478965{col 49}{space 1}   -1.35{col 58}{space 3}0.176{col 66}{space 4}-1.153014{col 79}{space 3} .2107151
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} 1.380601{col 38}{space 2} .4587578{col 49}{space 1}    3.01{col 58}{space 3}0.003{col 66}{space 4} .4814519{col 79}{space 3} 2.279749
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .0061841{col 38}{space 2} .0565721{col 49}{space 1}    0.11{col 58}{space 3}0.913{col 66}{space 4}-.1046952{col 79}{space 3} .1170635
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0018686{col 38}{space 2} .0045499{col 49}{space 1}   -0.41{col 58}{space 3}0.681{col 66}{space 4}-.0107862{col 79}{space 3}  .007049
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1635766{col 38}{space 2}  .114938{col 49}{space 1}    1.42{col 58}{space 3}0.155{col 66}{space 4}-.0616978{col 79}{space 3} .3888509
{txt}{space 18}income {c |}{col 26}{res}{space 2} -.061609{col 38}{space 2} .0587619{col 49}{space 1}   -1.05{col 58}{space 3}0.294{col 66}{space 4}-.1767801{col 79}{space 3} .0535621
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.1477674{col 38}{space 2} .1626213{col 49}{space 1}   -0.91{col 58}{space 3}0.364{col 66}{space 4}-.4664993{col 79}{space 3} .1709645
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} 1.276216{col 38}{space 2}  .080307{col 49}{space 1}   15.89{col 58}{space 3}0.000{col 66}{space 4} 1.118818{col 79}{space 3} 1.433615
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.2255947{col 38}{space 2} .1505244{col 49}{space 1}   -1.50{col 58}{space 3}0.134{col 66}{space 4}-.5206172{col 79}{space 3} .0694278
{txt}{space 20}kurd {c |}{col 26}{res}{space 2}-.3389051{col 38}{space 2} .2049433{col 49}{space 1}   -1.65{col 58}{space 3}0.098{col 66}{space 4}-.7405866{col 79}{space 3} .0627763
{txt}{space 20}wave {c |}{col 26}{res}{space 2}-.2253473{col 38}{space 2} .1525633{col 49}{space 1}   -1.48{col 58}{space 3}0.140{col 66}{space 4}-.5243659{col 79}{space 3} .0736713
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-2.180653{col 38}{space 2} .5951337{col 66}{space 4}-3.347094{col 79}{space 3}-1.014212
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2}-.4163714{col 38}{space 2} .5840094{col 66}{space 4}-1.561009{col 79}{space 3} .7282659
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 1.148141{col 38}{space 2} .5866432{col 66}{space 4}-.0016585{col 79}{space 3}  2.29794
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Table 7. Victimization and Support for LGBT+ Protections (OLS Regression, Fixed Effects)
. 
. xtset locale

{txt}{col 1}Panel variable: {res}locale{txt} (unbalanced)

{com}. xtreg revprotectgay victimorder dvictimindex wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1374{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0036{col 63}{txt}avg{col 67}={col 69}{res}      30.0
{txt}     Overall = {res}0.0787{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}3{txt}, {res}29{txt}){col 67}={col 70}{res}   137.90
{txt}corr(u_i, Xb) = {res}-0.4903{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .5890858{col 26}{space 2} .0450053{col 37}{space 1}   13.09{col 46}{space 3}0.000{col 54}{space 4} .4970395{col 67}{space 3}  .681132
{txt}dvictimindex {c |}{col 14}{res}{space 2} .4327243{col 26}{space 2} .1198383{col 37}{space 1}    3.61{col 46}{space 3}0.001{col 54}{space 4} .1876274{col 67}{space 3} .6778212
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.4579924{col 26}{space 2} .2455215{col 37}{space 1}   -1.87{col 46}{space 3}0.072{col 54}{space 4}-.9601403{col 67}{space 3} .0441555
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.669734{col 26}{space 2} .3864699{col 37}{space 1}    6.91{col 46}{space 3}0.000{col 54}{space 4} 1.879314{col 67}{space 3} 3.460154
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
     sigma_u {c |} {res} .43471535
     {txt}sigma_e {c |} {res} .65724925
         {txt}rho {c |} {res} .30433402{txt}   (fraction of variance due to u_i)
{hline 13}{c BT}{hline 64}

{com}. xtreg revprotectgay victimorder##dvictimindex wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1671{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0016{col 63}{txt}avg{col 67}={col 69}{res}      30.0
{txt}     Overall = {res}0.1184{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}4{txt}, {res}29{txt}){col 67}={col 70}{res}   152.05
{txt}corr(u_i, Xb) = {res}-0.2752{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2} -.226462{col 38}{space 2} .2145626{col 49}{space 1}   -1.06{col 58}{space 3}0.300{col 66}{space 4}-.6652919{col 79}{space 3} .2123678
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.2241046{col 38}{space 2}  .173204{col 49}{space 1}   -1.29{col 58}{space 3}0.206{col 66}{space 4}-.5783465{col 79}{space 3} .1301374
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .8735061{col 38}{space 2} .2259505{col 49}{space 1}    3.87{col 58}{space 3}0.001{col 66}{space 4} .4113854{col 79}{space 3} 1.335627
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2}-.1807452{col 38}{space 2} .1911206{col 49}{space 1}   -0.95{col 58}{space 3}0.352{col 66}{space 4}-.5716306{col 79}{space 3} .2101403
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.834262{col 38}{space 2} .2793721{col 49}{space 1}   10.15{col 58}{space 3}0.000{col 66}{space 4} 2.262882{col 79}{space 3} 3.405642
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} .40169199
                 {txt}sigma_e {c |} {res} .64619542
                     {txt}rho {c |} {res} .27871738{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. xtreg revprotectgay victimorder##dvictimindex gender age education income unemployed i.religion arab wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       898
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.2202{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0043{col 63}{txt}avg{col 67}={col 69}{res}      29.9
{txt}     Overall = {res}0.1697{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}11{txt}, {res}29{txt}){col 67}={col 70}{res}    97.89
{txt}corr(u_i, Xb) = {res}-0.2560{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.1920646{col 38}{space 2} .2140092{col 49}{space 1}   -0.90{col 58}{space 3}0.377{col 66}{space 4}-.6297626{col 79}{space 3} .2456334
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.2443754{col 38}{space 2} .1791541{col 49}{space 1}   -1.36{col 58}{space 3}0.183{col 66}{space 4}-.6107868{col 79}{space 3} .1220359
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .8814164{col 38}{space 2}  .233237{col 49}{space 1}    3.78{col 58}{space 3}0.001{col 66}{space 4} .4043931{col 79}{space 3}  1.35844
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .0084874{col 38}{space 2} .0230775{col 49}{space 1}    0.37{col 58}{space 3}0.716{col 66}{space 4}-.0387113{col 79}{space 3} .0556862
{txt}{space 21}age {c |}{col 26}{res}{space 2} -.003329{col 38}{space 2} .0029949{col 49}{space 1}   -1.11{col 58}{space 3}0.275{col 66}{space 4}-.0094543{col 79}{space 3} .0027962
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0486549{col 38}{space 2} .0666424{col 49}{space 1}    0.73{col 58}{space 3}0.471{col 66}{space 4}-.0876441{col 79}{space 3} .1849539
{txt}{space 18}income {c |}{col 26}{res}{space 2} -.017997{col 38}{space 2} .0326358{col 49}{space 1}   -0.55{col 58}{space 3}0.586{col 66}{space 4}-.0847447{col 79}{space 3} .0487508
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0393528{col 38}{space 2} .0998545{col 49}{space 1}   -0.39{col 58}{space 3}0.696{col 66}{space 4}-.2435782{col 79}{space 3} .1648727
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .7572295{col 38}{space 2} .0485518{col 49}{space 1}   15.60{col 58}{space 3}0.000{col 66}{space 4}   .65793{col 79}{space 3} .8565291
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.0103483{col 38}{space 2} .0667572{col 49}{space 1}   -0.16{col 58}{space 3}0.878{col 66}{space 4}-.1468821{col 79}{space 3} .1261854
{txt}{space 20}wave {c |}{col 26}{res}{space 2}-.2160489{col 38}{space 2} .1936734{col 49}{space 1}   -1.12{col 58}{space 3}0.274{col 66}{space 4}-.6121555{col 79}{space 3} .1800577
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.830224{col 38}{space 2} .3593327{col 49}{space 1}    7.88{col 58}{space 3}0.000{col 66}{space 4} 2.095306{col 79}{space 3} 3.565142
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} .39333709
                 {txt}sigma_e {c |} {res} .62817766
                     {txt}rho {c |} {res} .28164588{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. 
. *(for time/location fixed effects also can use gen =wave*locale) xtset wavelocale and cluster(wavelocale)
. 
. xtset wavelocale

{txt}{col 1}Panel variable: {res}wavelocale{txt} (unbalanced)

{com}. xtreg revprotectgay victimorder dvictimindex wave, fe vce(cluster wavelocale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}wavelocale{txt}{col 49}Number of groups{col 67}={col 69}{res}        29

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1303{col 63}{txt}min{col 67}={col 69}{res}        10
{txt}     Between = {res}0.0040{col 63}{txt}avg{col 67}={col 69}{res}      31.0
{txt}     Overall = {res}0.0785{col 63}{txt}max{col 67}={col 69}{res}        85

{txt}{col 49}F({res}3{txt}, {res}28{txt}){col 67}={col 70}{res}    72.32
{txt}corr(u_i, Xb) = {res}-0.3842{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:29} clusters in {res:wavelocale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .5797033{col 26}{space 2} .0454304{col 37}{space 1}   12.76{col 46}{space 3}0.000{col 54}{space 4} .4866433{col 67}{space 3} .6727634
{txt}dvictimindex {c |}{col 14}{res}{space 2} .4771747{col 26}{space 2} .1195945{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .2321965{col 67}{space 3}  .722153
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.0396069{col 26}{space 2} .3498685{col 37}{space 1}   -0.11{col 46}{space 3}0.911{col 54}{space 4}  -.75628{col 67}{space 3} .6770661
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.939778{col 26}{space 2} .5784162{col 37}{space 1}    3.35{col 46}{space 3}0.002{col 54}{space 4} .7549457{col 67}{space 3} 3.124609
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
     sigma_u {c |} {res} .37465985
     {txt}sigma_e {c |} {res}  .6652144
         {txt}rho {c |} {res} .24082136{txt}   (fraction of variance due to u_i)
{hline 13}{c BT}{hline 64}

{com}. xtreg revprotectgay victimorder##dvictimindex wave, fe vce(cluster wavelocale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}wavelocale{txt}{col 49}Number of groups{col 67}={col 69}{res}        29

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1523{col 63}{txt}min{col 67}={col 69}{res}        10
{txt}     Between = {res}0.0056{col 63}{txt}avg{col 67}={col 69}{res}      31.0
{txt}     Overall = {res}0.1153{col 63}{txt}max{col 67}={col 69}{res}        85

{txt}{col 49}F({res}4{txt}, {res}28{txt}){col 67}={col 70}{res}    56.78
{txt}corr(u_i, Xb) = {res}-0.2361{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:29} clusters in {res:wavelocale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.1704186{col 38}{space 2} .1955747{col 49}{space 1}   -0.87{col 58}{space 3}0.391{col 66}{space 4}-.5710351{col 79}{space 3} .2301979
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.1310401{col 38}{space 2} .1731738{col 49}{space 1}   -0.76{col 58}{space 3}0.456{col 66}{space 4}-.4857705{col 79}{space 3} .2236903
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .7863504{col 38}{space 2} .2021678{col 49}{space 1}    3.89{col 58}{space 3}0.001{col 66}{space 4} .3722283{col 79}{space 3} 1.200472
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2}-.0492806{col 38}{space 2} .3498921{col 49}{space 1}   -0.14{col 58}{space 3}0.889{col 66}{space 4}-.7660021{col 79}{space 3} .6674408
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.542175{col 38}{space 2} .6156637{col 49}{space 1}    4.13{col 58}{space 3}0.000{col 66}{space 4} 1.281045{col 79}{space 3} 3.803305
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} .33511156
                 {txt}sigma_e {c |} {res} .65711092
                     {txt}rho {c |} {res} .20639752{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. xtreg revprotectgay victimorder##dvictimindex gender age education income unemployed i.religion arab wave, fe vce(cluster wavelocale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       898
{txt}Group variable: {res}wavelocale{txt}{col 49}Number of groups{col 67}={col 69}{res}        29

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.2101{col 63}{txt}min{col 67}={col 69}{res}        10
{txt}     Between = {res}0.0199{col 63}{txt}avg{col 67}={col 69}{res}      31.0
{txt}     Overall = {res}0.1661{col 63}{txt}max{col 67}={col 69}{res}        85

{txt}{col 49}F({res}11{txt}, {res}28{txt}){col 67}={col 70}{res}   156.44
{txt}corr(u_i, Xb) = {res}-0.2371{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:29} clusters in {res:wavelocale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revprotectgays{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.1275186{col 38}{space 2} .1956267{col 49}{space 1}   -0.65{col 58}{space 3}0.520{col 66}{space 4}-.5282416{col 79}{space 3} .2732045
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.1426365{col 38}{space 2} .1701351{col 49}{space 1}   -0.84{col 58}{space 3}0.409{col 66}{space 4}-.4911424{col 79}{space 3} .2058694
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .7870423{col 38}{space 2} .2044722{col 49}{space 1}    3.85{col 58}{space 3}0.001{col 66}{space 4}    .3682{col 79}{space 3} 1.205885
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2} .0224774{col 38}{space 2} .0264808{col 49}{space 1}    0.85{col 58}{space 3}0.403{col 66}{space 4}-.0317662{col 79}{space 3} .0767209
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0032691{col 38}{space 2} .0031367{col 49}{space 1}   -1.04{col 58}{space 3}0.306{col 66}{space 4}-.0096943{col 79}{space 3} .0031561
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0569472{col 38}{space 2} .0707209{col 49}{space 1}    0.81{col 58}{space 3}0.427{col 66}{space 4}-.0879179{col 79}{space 3} .2018124
{txt}{space 18}income {c |}{col 26}{res}{space 2}  -.02968{col 38}{space 2} .0301662{col 49}{space 1}   -0.98{col 58}{space 3}0.334{col 66}{space 4}-.0914726{col 79}{space 3} .0321126
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0578216{col 38}{space 2} .1148537{col 49}{space 1}   -0.50{col 58}{space 3}0.619{col 66}{space 4}-.2930887{col 79}{space 3} .1774455
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .7886319{col 38}{space 2}  .040123{col 49}{space 1}   19.66{col 58}{space 3}0.000{col 66}{space 4} .7064437{col 79}{space 3}   .87082
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.0367614{col 38}{space 2} .0552182{col 49}{space 1}   -0.67{col 58}{space 3}0.511{col 66}{space 4}-.1498707{col 79}{space 3} .0763479
{txt}{space 20}wave {c |}{col 26}{res}{space 2}  -.05851{col 38}{space 2} .3298054{col 49}{space 1}   -0.18{col 58}{space 3}0.860{col 66}{space 4}-.7340858{col 79}{space 3} .6170658
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.508827{col 38}{space 2} .5677266{col 49}{space 1}    4.42{col 58}{space 3}0.000{col 66}{space 4} 1.345891{col 79}{space 3} 3.671762
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} .33819953
                 {txt}sigma_e {c |} {res} .63737707
                     {txt}rho {c |} {res} .21969396{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. 
. *Table 8. Victimization Components and Support for LGBT+ Protections (OLS Regression)
. 
. reg revprotectgay victimorder punishedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    19.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0885
                                                {txt}Root MSE          =    {res} .69349

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .2506803{col 26}{space 2} .0673974{col 37}{space 1}    3.72{col 46}{space 3}0.001{col 54}{space 4} .1128371{col 67}{space 3} .3885234
{txt}punishedisis {c |}{col 14}{res}{space 2} .2928651{col 26}{space 2} .0544209{col 37}{space 1}    5.38{col 46}{space 3}0.000{col 54}{space 4} .1815618{col 67}{space 3} .4041684
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.324236{col 26}{space 2} .0745223{col 37}{space 1}   31.19{col 46}{space 3}0.000{col 54}{space 4} 2.171821{col 67}{space 3} 2.476651
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder fampunishedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    16.69
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1555
                                                {txt}Root MSE          =    {res} .66749

{txt}{ralign 81:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revprotectgays{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}victimorder {c |}{col 17}{res}{space 2} .2791965{col 29}{space 2} .0736263{col 40}{space 1}    3.79{col 49}{space 3}0.001{col 57}{space 4} .1286137{col 70}{space 3} .4297793
{txt}fampunishedisis {c |}{col 17}{res}{space 2} .4684916{col 29}{space 2} .0822243{col 40}{space 1}    5.70{col 49}{space 3}0.000{col 57}{space 4}  .300324{col 70}{space 3} .6366593
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.202052{col 29}{space 2} .1100515{col 40}{space 1}   20.01{col 49}{space 3}0.000{col 57}{space 4} 1.976972{col 70}{space 3} 2.427133
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder injuredisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}     8.84
                                                {txt}Prob > F          = {res}    0.0010
                                                {txt}R-squared         = {res}    0.0715
                                                {txt}Root MSE          =    {res} .69991

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .4141157{col 26}{space 2}  .104348{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .2007001{col 67}{space 3} .6275314
{txt}{space 1}injuredisis {c |}{col 14}{res}{space 2} .3161051{col 26}{space 2}  .100921{col 37}{space 1}    3.13{col 46}{space 3}0.004{col 54}{space 4} .1096984{col 67}{space 3} .5225117
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.31559{col 26}{space 2} .0893762{col 37}{space 1}   25.91{col 46}{space 3}0.000{col 54}{space 4} 2.132796{col 67}{space 3} 2.498385
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder faminjuredisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}     9.78
                                                {txt}Prob > F          = {res}    0.0006
                                                {txt}R-squared         = {res}    0.1678
                                                {txt}Root MSE          =    {res} .66261

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revprotectgays{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .4238095{col 28}{space 2} .0961124{col 39}{space 1}    4.41{col 48}{space 3}0.000{col 56}{space 4} .2272376{col 69}{space 3} .6203815
{txt}faminjuredisis {c |}{col 16}{res}{space 2} .9047619{col 28}{space 2} .2407703{col 39}{space 1}    3.76{col 48}{space 3}0.001{col 56}{space 4} .4123313{col 69}{space 3} 1.397193
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.277143{col 28}{space 2} .0741006{col 39}{space 1}   30.73{col 48}{space 3}0.000{col 56}{space 4}  2.12559{col 69}{space 3} 2.428696
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder famkilledisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    14.18
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0538
                                                {txt}Root MSE          =    {res} .70657

{txt}{ralign 79:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revprotectg~s{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}victimorder {c |}{col 15}{res}{space 2}  .333948{col 27}{space 2} .0743639{col 38}{space 1}    4.49{col 47}{space 3}0.000{col 55}{space 4} .1818568{col 68}{space 3} .4860392
{txt}famkilledisis {c |}{col 15}{res}{space 2}-.0553207{col 27}{space 2} .0457498{col 38}{space 1}   -1.21{col 47}{space 3}0.236{col 55}{space 4}-.1488896{col 68}{space 3} .0382481
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.408596{col 27}{space 2} .0598156{col 38}{space 1}   40.27{col 47}{space 3}0.000{col 55}{space 4} 2.286259{col 68}{space 3} 2.530933
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder imprisonedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    25.58
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0871
                                                {txt}Root MSE          =    {res} .69399

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revprotectgays{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .2063793{col 28}{space 2} .0691998{col 39}{space 1}    2.98{col 48}{space 3}0.006{col 56}{space 4} .0648498{col 69}{space 3} .3479088
{txt}imprisonedisis {c |}{col 16}{res}{space 2} .3173851{col 28}{space 2} .0561899{col 39}{space 1}    5.65{col 48}{space 3}0.000{col 56}{space 4} .2024639{col 69}{space 3} .4323063
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.358281{col 28}{space 2} .0545445{col 39}{space 1}   43.24{col 48}{space 3}0.000{col 56}{space 4} 2.246725{col 69}{space 3} 2.469837
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder fledhomeisis , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    26.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0647
                                                {txt}Root MSE          =    {res} .70247

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revprotect~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .3021605{col 26}{space 2} .0697708{col 37}{space 1}    4.33{col 46}{space 3}0.000{col 54}{space 4} .1594632{col 67}{space 3} .4448577
{txt}fledhomeisis {c |}{col 14}{res}{space 2}-.1650329{col 26}{space 2} .0334544{col 37}{space 1}   -4.93{col 46}{space 3}0.000{col 54}{space 4}-.2334548{col 67}{space 3}-.0966109
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.478094{col 26}{space 2} .0566061{col 37}{space 1}   43.78{col 46}{space 3}0.000{col 54}{space 4} 2.362321{col 67}{space 3} 2.593866
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder homelootedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    10.28
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0657
                                                {txt}Root MSE          =    {res} .70208

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revprotectgays{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .3364217{col 28}{space 2} .0801885{col 39}{space 1}    4.20{col 48}{space 3}0.000{col 56}{space 4} .1724178{col 69}{space 3} .5004255
{txt}homelootedisis {c |}{col 16}{res}{space 2} .1737184{col 28}{space 2} .0909898{col 39}{space 1}    1.91{col 48}{space 3}0.066{col 56}{space 4}-.0123766{col 69}{space 3} .3598134
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.281966{col 28}{space 2} .1236423{col 39}{space 1}   18.46{col 48}{space 3}0.000{col 56}{space 4} 2.029089{col 69}{space 3} 2.534842
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revprotectgay victimorder womenabusedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}     8.61
                                                {txt}Prob > F          = {res}    0.0012
                                                {txt}R-squared         = {res}    0.1156
                                                {txt}Root MSE          =    {res} .68308

{txt}{ralign 81:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revprotectgays{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}victimorder {c |}{col 17}{res}{space 2} .3128238{col 29}{space 2} .0779379{col 40}{space 1}    4.01{col 49}{space 3}0.000{col 57}{space 4} .1534229{col 70}{space 3} .4722246
{txt}womenabusedisis {c |}{col 17}{res}{space 2}  .369172{col 29}{space 2} .1057848{col 40}{space 1}    3.49{col 49}{space 3}0.002{col 57}{space 4} .1528179{col 70}{space 3} .5855261
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.192682{col 29}{space 2} .1304431{col 40}{space 1}   16.81{col 49}{space 3}0.000{col 57}{space 4} 1.925896{col 70}{space 3} 2.459468
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Alternate Dependent Variable – Support for Human Rights
. 
. *Figure Human Rights Histogram
. 
. histogram revhumanrights, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. *Table 9. Support for Basic Human Rights (Ordered Probit Regression)
. 
. oprobit revhumanrights victimorder wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-983.66404}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-971.38797}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-971.38271}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-971.38271}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:18.02}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0001}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-971.38271}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0125}

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revhumanrights{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .3651006{col 28}{space 2} .0865241{col 39}{space 1}    4.22{col 48}{space 3}0.000{col 56}{space 4} .1955165{col 69}{space 3} .5346847
{txt}{space 10}wave {c |}{col 16}{res}{space 2}-.0136778{col 28}{space 2} .1023133{col 39}{space 1}   -0.13{col 48}{space 3}0.894{col 56}{space 4}-.2142081{col 69}{space 3} .1868526
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-1.456187{col 28}{space 2} .1913724{col 56}{space 4} -1.83127{col 69}{space 3}-1.081104
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-.0024669{col 28}{space 2} .2134025{col 56}{space 4}-.4207282{col 69}{space 3} .4157944
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.547187{col 28}{space 2} .2255417{col 56}{space 4} 1.105133{col 69}{space 3} 1.989241
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revhumanrights victimorder dvictimindex wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-983.66404}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -963.5937}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-963.58013}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-963.58013}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:3})}{col 70} = {res}{ralign 6:19.38}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0002}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-963.58013}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0204}

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revhumanrights{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .4343417{col 28}{space 2} .1180098{col 39}{space 1}    3.68{col 48}{space 3}0.000{col 56}{space 4} .2030467{col 69}{space 3} .6656367
{txt}{space 2}dvictimindex {c |}{col 16}{res}{space 2} .4148422{col 28}{space 2} .2113034{col 39}{space 1}    1.96{col 48}{space 3}0.050{col 56}{space 4} .0006952{col 69}{space 3} .8289892
{txt}{space 10}wave {c |}{col 16}{res}{space 2}-.0872494{col 28}{space 2} .1054801{col 39}{space 1}   -0.83{col 48}{space 3}0.408{col 56}{space 4}-.2939867{col 69}{space 3} .1194878
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-1.213518{col 28}{space 2}  .252829{col 56}{space 4}-1.709053{col 69}{space 3}-.7179819
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .2562697{col 28}{space 2} .2825507{col 56}{space 4}-.2975196{col 69}{space 3} .8100589
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.820682{col 28}{space 2} .3262845{col 56}{space 4} 1.181176{col 69}{space 3} 2.460188
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revhumanrights victimorder##dvictimindex wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-983.66404}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-943.37642}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-943.30368}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-943.30367}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:900}
{txt}{col 57}{lalign 13:Wald chi2({res:4})}{col 70} = {res}{ralign 6:28.02}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-943.30367}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0410}

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      z{col 58}   P>|z|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.8468798{col 38}{space 2} .4548131{col 49}{space 1}   -1.86{col 58}{space 3}0.063{col 66}{space 4}-1.738297{col 79}{space 3} .0445375
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.6512027{col 38}{space 2}  .411365{col 49}{space 1}   -1.58{col 58}{space 3}0.113{col 66}{space 4}-1.457463{col 79}{space 3} .1550578
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} 1.478081{col 38}{space 2}  .510453{col 49}{space 1}    2.90{col 58}{space 3}0.004{col 66}{space 4} .4776119{col 79}{space 3} 2.478551
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2} .0228112{col 38}{space 2} .0965758{col 49}{space 1}    0.24{col 58}{space 3}0.813{col 66}{space 4}-.1664738{col 79}{space 3} .2120963
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-2.046978{col 38}{space 2} .4339837{col 66}{space 4}-2.897571{col 79}{space 3}-1.196386
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2} -.539176{col 38}{space 2} .4433926{col 66}{space 4} -1.40821{col 79}{space 3} .3298576
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} 1.069683{col 38}{space 2} .4325227{col 66}{space 4} .2219542{col 79}{space 3} 1.917412
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. oprobit revhumanrights victimorder##dvictimindex gender age education income unemployed  i.religion arab kurd wave, vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-982.20295}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-888.60411}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-888.06745}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-888.06705}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-888.06705}  
{res}
{txt}{col 1}Ordered probit regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:898}
{txt}{col 57}{lalign 13:Wald chi2({res:12})}{col 70} = {res}{ralign 6:198.14}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-888.06705}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0958}

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      z{col 58}   P>|z|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-1.029319{col 38}{space 2} .5162416{col 49}{space 1}   -1.99{col 58}{space 3}0.046{col 66}{space 4}-2.041134{col 79}{space 3}-.0175039
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.8788797{col 38}{space 2} .4749267{col 49}{space 1}   -1.85{col 58}{space 3}0.064{col 66}{space 4}-1.809719{col 79}{space 3} .0519595
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} 1.639978{col 38}{space 2} .5784597{col 49}{space 1}    2.84{col 58}{space 3}0.005{col 66}{space 4} .5062183{col 79}{space 3} 2.773739
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2}-.0120098{col 38}{space 2} .0354294{col 49}{space 1}   -0.34{col 58}{space 3}0.735{col 66}{space 4}-.0814502{col 79}{space 3} .0574306
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0045041{col 38}{space 2} .0053021{col 49}{space 1}   -0.85{col 58}{space 3}0.396{col 66}{space 4}-.0148959{col 79}{space 3} .0058878
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1550831{col 38}{space 2} .0965454{col 49}{space 1}    1.61{col 58}{space 3}0.108{col 66}{space 4}-.0341424{col 79}{space 3} .3443086
{txt}{space 18}income {c |}{col 26}{res}{space 2}-.0782001{col 38}{space 2} .0550556{col 49}{space 1}   -1.42{col 58}{space 3}0.155{col 66}{space 4}-.1861072{col 79}{space 3} .0297069
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0694593{col 38}{space 2} .1961132{col 49}{space 1}   -0.35{col 58}{space 3}0.723{col 66}{space 4} -.453834{col 79}{space 3} .3149155
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .9406553{col 38}{space 2} .0798983{col 49}{space 1}   11.77{col 58}{space 3}0.000{col 66}{space 4} .7840576{col 79}{space 3} 1.097253
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.0789189{col 38}{space 2} .1865748{col 49}{space 1}   -0.42{col 58}{space 3}0.672{col 66}{space 4}-.4445989{col 79}{space 3}  .286761
{txt}{space 20}kurd {c |}{col 26}{res}{space 2}-1.064817{col 38}{space 2} .3260576{col 49}{space 1}   -3.27{col 58}{space 3}0.001{col 66}{space 4}-1.703878{col 79}{space 3}-.4257558
{txt}{space 20}wave {c |}{col 26}{res}{space 2}-.0287287{col 38}{space 2} .1009568{col 49}{space 1}   -0.28{col 58}{space 3}0.776{col 66}{space 4}-.2266003{col 79}{space 3} .1691429
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/cut1 {c |}{col 26}{res}{space 2}-2.474689{col 38}{space 2} .8133996{col 66}{space 4}-4.068922{col 79}{space 3}-.8804548
{txt}{space 19}/cut2 {c |}{col 26}{res}{space 2}-.7705289{col 38}{space 2} .7789882{col 66}{space 4}-2.297318{col 79}{space 3}   .75626
{txt}{space 19}/cut3 {c |}{col 26}{res}{space 2} .8837438{col 38}{space 2} .7541584{col 66}{space 4}-.5943796{col 79}{space 3} 2.361867
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Table 10. Victimization and Support for LGBT+ Protections (OLS Regression, Fixed Effects)
. 
. xtset locale

{txt}{col 1}Panel variable: {res}locale{txt} (unbalanced)

{com}. xtreg revhumanrights victimorder dvictimindex wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.0761{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0044{col 63}{txt}avg{col 67}={col 69}{res}      30.0
{txt}     Overall = {res}0.0330{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}3{txt}, {res}29{txt}){col 67}={col 70}{res}    63.44
{txt}corr(u_i, Xb) = {res}-0.5776{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .4505899{col 26}{space 2} .0426987{col 37}{space 1}   10.55{col 46}{space 3}0.000{col 54}{space 4} .3632613{col 67}{space 3} .5379184
{txt}dvictimindex {c |}{col 14}{res}{space 2} .3190267{col 26}{space 2} .1401525{col 37}{space 1}    2.28{col 46}{space 3}0.030{col 54}{space 4} .0323826{col 67}{space 3} .6056707
{txt}{space 8}wave {c |}{col 14}{res}{space 2}-.3750851{col 26}{space 2} .2199214{col 37}{space 1}   -1.71{col 46}{space 3}0.099{col 54}{space 4}-.8248749{col 67}{space 3} .0747048
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.725198{col 26}{space 2} .3789037{col 37}{space 1}    7.19{col 46}{space 3}0.000{col 54}{space 4} 1.950253{col 67}{space 3} 3.500143
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
     sigma_u {c |} {res} .33139302
     {txt}sigma_e {c |} {res} .69706542
         {txt}rho {c |} {res} .18435012{txt}   (fraction of variance due to u_i)
{hline 13}{c BT}{hline 64}

{com}. xtreg revhumanrights victimorder##dvictimindex wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       900
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.1125{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0054{col 63}{txt}avg{col 67}={col 69}{res}      30.0
{txt}     Overall = {res}0.0875{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}4{txt}, {res}29{txt}){col 67}={col 70}{res}    74.84
{txt}corr(u_i, Xb) = {res}-0.2500{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.4737318{col 38}{space 2} .2779825{col 49}{space 1}   -1.70{col 58}{space 3}0.099{col 66}{space 4} -1.04227{col 79}{space 3} .0948062
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.4254069{col 38}{space 2} .2332971{col 49}{space 1}   -1.82{col 58}{space 3}0.079{col 66}{space 4}-.9025531{col 79}{space 3} .0517393
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .9900102{col 38}{space 2}  .296145{col 49}{space 1}    3.34{col 58}{space 3}0.002{col 66}{space 4} .3843256{col 79}{space 3} 1.595695
{txt}{space 24} {c |}
{space 20}wave {c |}{col 26}{res}{space 2}-.0608599{col 38}{space 2} .1697516{col 49}{space 1}   -0.36{col 58}{space 3}0.723{col 66}{space 4} -.408041{col 79}{space 3} .2863211
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}  2.91167{col 38}{space 2} .3681689{col 49}{space 1}    7.91{col 58}{space 3}0.000{col 66}{space 4}  2.15868{col 79}{space 3}  3.66466
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} .27323308
                 {txt}sigma_e {c |} {res} .68360282
                     {txt}rho {c |} {res} .13775012{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. xtreg revhumanrights victimorder##dvictimindex gender age education income unemployed i.religion arab wave, fe vce(cluster locale)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       898
{txt}Group variable: {res}locale{txt}{col 49}Number of groups{col 67}={col 69}{res}        30

{txt}R-squared:{col 49}Obs per group:
     Within  = {res}0.2015{col 63}{txt}min{col 67}={col 69}{res}         9
{txt}     Between = {res}0.0000{col 63}{txt}avg{col 67}={col 69}{res}      29.9
{txt}     Overall = {res}0.1721{col 63}{txt}max{col 67}={col 69}{res}        91

{txt}{col 49}F({res}11{txt}, {res}29{txt}){col 67}={col 70}{res}    74.03
{txt}corr(u_i, Xb) = {res}-0.2050{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{ralign 90:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}          revhumanrights{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2}-.5364186{col 38}{space 2} .2733837{col 49}{space 1}   -1.96{col 58}{space 3}0.059{col 66}{space 4}-1.095551{col 79}{space 3} .0227138
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.5378595{col 38}{space 2} .2431713{col 49}{space 1}   -2.21{col 58}{space 3}0.035{col 66}{space 4}-1.035201{col 79}{space 3}-.0405183
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} 1.065491{col 38}{space 2} .2993565{col 49}{space 1}    3.56{col 58}{space 3}0.001{col 66}{space 4} .4532382{col 79}{space 3} 1.677744
{txt}{space 24} {c |}
{space 18}gender {c |}{col 26}{res}{space 2}-.0606233{col 38}{space 2}   .03158{col 49}{space 1}   -1.92{col 58}{space 3}0.065{col 66}{space 4}-.1252116{col 79}{space 3}  .003965
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0026689{col 38}{space 2} .0035724{col 49}{space 1}   -0.75{col 58}{space 3}0.461{col 66}{space 4}-.0099752{col 79}{space 3} .0046374
{txt}{space 15}education {c |}{col 26}{res}{space 2} .0722912{col 38}{space 2} .0607998{col 49}{space 1}    1.19{col 58}{space 3}0.244{col 66}{space 4}-.0520583{col 79}{space 3} .1966408
{txt}{space 18}income {c |}{col 26}{res}{space 2}-.0403014{col 38}{space 2} .0309409{col 49}{space 1}   -1.30{col 58}{space 3}0.203{col 66}{space 4}-.1035826{col 79}{space 3} .0229798
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2}-.0284011{col 38}{space 2} .1200427{col 49}{space 1}   -0.24{col 58}{space 3}0.815{col 66}{space 4}-.2739161{col 79}{space 3} .2171139
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2} .5894427{col 38}{space 2} .0412598{col 49}{space 1}   14.29{col 58}{space 3}0.000{col 66}{space 4} .5050569{col 79}{space 3} .6738284
{txt}{space 20}arab {c |}{col 26}{res}{space 2} .4495796{col 38}{space 2}  .113135{col 49}{space 1}    3.97{col 58}{space 3}0.000{col 66}{space 4} .2181925{col 79}{space 3} .6809667
{txt}{space 20}wave {c |}{col 26}{res}{space 2}-.0394133{col 38}{space 2}  .172305{col 49}{space 1}   -0.23{col 58}{space 3}0.821{col 66}{space 4}-.3918166{col 79}{space 3} .3129901
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.509294{col 38}{space 2}  .450658{col 49}{space 1}    5.57{col 58}{space 3}0.000{col 66}{space 4} 1.587595{col 79}{space 3} 3.430993
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res}  .2732818
                 {txt}sigma_e {c |} {res} .65150917
                     {txt}rho {c |} {res} .14962109{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}

{com}. 
. *Table 11. Victimization Components and Support for LGBT+ Protections (OLS Regression)
. 
. reg revhumanrights victimorder punishedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    54.05
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1180
                                                {txt}Root MSE          =    {res}  .6821

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .1186337{col 26}{space 2} .0478464{col 37}{space 1}    2.48{col 46}{space 3}0.019{col 54}{space 4} .0207768{col 67}{space 3} .2164906
{txt}punishedisis {c |}{col 14}{res}{space 2} .4615341{col 26}{space 2} .0448014{col 37}{space 1}   10.30{col 46}{space 3}0.000{col 54}{space 4}  .369905{col 67}{space 3} .5531632
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.352993{col 26}{space 2} .0579583{col 37}{space 1}   40.60{col 46}{space 3}0.000{col 54}{space 4} 2.234455{col 67}{space 3} 2.471531
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder fampunishedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    27.32
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1148
                                                {txt}Root MSE          =    {res} .68332

{txt}{ralign 81:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revhumanrights{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}victimorder {c |}{col 17}{res}{space 2} .1995364{col 29}{space 2} .0535675{col 40}{space 1}    3.72{col 49}{space 3}0.001{col 57}{space 4} .0899786{col 70}{space 3} .3090941
{txt}fampunishedisis {c |}{col 17}{res}{space 2} .4270892{col 29}{space 2} .0578085{col 40}{space 1}    7.39{col 49}{space 3}0.000{col 57}{space 4} .3088576{col 70}{space 3} .5453208
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.290461{col 29}{space 2} .0776453{col 40}{space 1}   29.50{col 49}{space 3}0.000{col 57}{space 4} 2.131658{col 70}{space 3} 2.449263
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder injuredisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    72.42
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1233
                                                {txt}Root MSE          =    {res} .68005

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2}  .429811{col 26}{space 2} .0894093{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .2469485{col 67}{space 3} .6126734
{txt}{space 1}injuredisis {c |}{col 14}{res}{space 2} .7079559{col 26}{space 2} .0769428{col 37}{space 1}    9.20{col 46}{space 3}0.000{col 54}{space 4} .5505903{col 67}{space 3} .8653216
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.28482{col 26}{space 2} .0728971{col 37}{space 1}   31.34{col 46}{space 3}0.000{col 54}{space 4} 2.135729{col 67}{space 3} 2.433912
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder faminjuredisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}     9.30
                                                {txt}Prob > F          = {res}    0.0008
                                                {txt}R-squared         = {res}    0.1259
                                                {txt}Root MSE          =    {res} .67901

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revhumanrights{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2}  .331746{col 28}{space 2} .0770221{col 39}{space 1}    4.31{col 48}{space 3}0.000{col 56}{space 4} .1742181{col 69}{space 3}  .489274
{txt}faminjuredisis {c |}{col 16}{res}{space 2} .8285714{col 28}{space 2} .2257691{col 39}{space 1}    3.67{col 48}{space 3}0.001{col 56}{space 4} .3668217{col 69}{space 3} 1.290321
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.358413{col 28}{space 2} .0559324{col 39}{space 1}   42.17{col 48}{space 3}0.000{col 56}{space 4} 2.244018{col 69}{space 3} 2.472807
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder famkilledisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    15.83
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0332
                                                {txt}Root MSE          =    {res} .71413

{txt}{ralign 79:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revhumanrig~s{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}victimorder {c |}{col 15}{res}{space 2} .2501198{col 27}{space 2} .0564257{col 38}{space 1}    4.43{col 47}{space 3}0.000{col 55}{space 4} .1347162{col 68}{space 3} .3655234
{txt}famkilledisis {c |}{col 15}{res}{space 2}-.1107798{col 27}{space 2} .0493173{col 38}{space 1}   -2.25{col 47}{space 3}0.032{col 55}{space 4}-.2116451{col 68}{space 3}-.0099145
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.490552{col 27}{space 2} .0426403{col 38}{space 1}   58.41{col 47}{space 3}0.000{col 55}{space 4} 2.403343{col 68}{space 3} 2.577762
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder imprisonedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    23.45
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0552
                                                {txt}Root MSE          =    {res} .70594

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revhumanrights{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2}   .13889{col 28}{space 2} .0498737{col 39}{space 1}    2.78{col 48}{space 3}0.009{col 56}{space 4} .0368868{col 69}{space 3} .2408931
{txt}imprisonedisis {c |}{col 16}{res}{space 2} .2749972{col 28}{space 2} .0511187{col 39}{space 1}    5.38{col 48}{space 3}0.000{col 56}{space 4} .1704478{col 69}{space 3} .3795467
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.434667{col 28}{space 2} .0368666{col 39}{space 1}   66.04{col 48}{space 3}0.000{col 56}{space 4} 2.359266{col 69}{space 3} 2.510068
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder fledhomeisis , cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    32.97
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0358
                                                {txt}Root MSE          =    {res} .71317

{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revhumanri~s{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}victimorder {c |}{col 14}{res}{space 2} .2261429{col 26}{space 2} .0553321{col 37}{space 1}    4.09{col 46}{space 3}0.000{col 54}{space 4} .1129759{col 67}{space 3} .3393098
{txt}fledhomeisis {c |}{col 14}{res}{space 2}-.1204201{col 26}{space 2} .0346396{col 37}{space 1}   -3.48{col 46}{space 3}0.002{col 54}{space 4}-.1912661{col 67}{space 3}-.0495741
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.527493{col 26}{space 2} .0455216{col 37}{space 1}   55.52{col 46}{space 3}0.000{col 54}{space 4} 2.434391{col 67}{space 3} 2.620595
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder homelootedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    13.78
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0297
                                                {txt}Root MSE          =    {res} .71542

{txt}{ralign 80:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revhumanrights{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}victimorder {c |}{col 16}{res}{space 2} .2493052{col 28}{space 2} .0560229{col 39}{space 1}    4.45{col 48}{space 3}0.000{col 56}{space 4} .1347255{col 69}{space 3} .3638848
{txt}homelootedisis {c |}{col 16}{res}{space 2} .0234156{col 28}{space 2} .0623274{col 39}{space 1}    0.38{col 48}{space 3}0.710{col 56}{space 4}-.1040583{col 69}{space 3} .1508894
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 2.453279{col 28}{space 2} .0751314{col 39}{space 1}   32.65{col 48}{space 3}0.000{col 56}{space 4} 2.299617{col 69}{space 3}  2.60694
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revhumanrights victimorder womenabusedisis, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       900
                                                {txt}F(2, 29)          =  {res}    10.31
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0735
                                                {txt}Root MSE          =    {res}  .6991

{txt}{ralign 81:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revhumanrights{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      t{col 49}   P>|t|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}victimorder {c |}{col 17}{res}{space 2} .2317197{col 29}{space 2} .0559994{col 40}{space 1}    4.14{col 49}{space 3}0.000{col 57}{space 4} .1171882{col 70}{space 3} .3462513
{txt}womenabusedisis {c |}{col 17}{res}{space 2} .3090446{col 29}{space 2}  .075365{col 40}{space 1}    4.10{col 49}{space 3}0.000{col 57}{space 4} .1549058{col 70}{space 3} .4631834
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.297197{col 29}{space 2} .0883303{col 40}{space 1}   26.01{col 49}{space 3}0.000{col 57}{space 4} 2.116542{col 70}{space 3} 2.477853
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Local Understandings of Homosexuality
. 
. *Figures
. 
. histogram defgayman, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g4.gph
{res}{txt}file {bf:g4.gph} saved

{com}. histogram deflesbian, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g5.gph
{res}{txt}file {bf:g5.gph} saved

{com}. graph combine "g4.gph" "g5.gph"
{res}{txt}
{com}. 
. histogram defbisexual, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g6.gph
{res}{txt}file {bf:g6.gph} saved

{com}. histogram deftrans, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g7.gph
{res}{txt}file {bf:g7.gph} saved

{com}. graph combine "g6.gph" "g7.gph"
{res}{txt}
{com}. 
. histogram defandrog, discrete percent addlabels
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. factor defgay-defandrog
{txt}(obs=300)

Factor analysis/correlation{col 50}Number of obs    = {res}       300
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       9

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.59523      1.57606            1.2945       1.2945
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.01916      0.05365            0.0156       1.3100
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.03449      0.09053           -0.0280       1.2820
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.12502      0.09751           -0.1014       1.1806
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.22253            .           -0.1806       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}10{txt}) ={res}  257.15{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:defgayman}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5922}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0175}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6490}}}{space 1}
{space 4}{space 0}{ralign 12:deflesbian}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6012}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0104}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6384}}}{space 1}
{space 4}{space 0}{ralign 12:defbisexual}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3608}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1114}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8574}}}{space 1}
{space 4}{space 0}{ralign 12:deftrans}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6971}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0046}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5140}}}{space 1}
{space 4}{space 0}{ralign 12:defandrog}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5166}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0796}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7268}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. graph save g8.gph
{res}{txt}file {bf:g8.gph} saved

{com}. 
. *Table 12. Correlates of Ambiguity about LGBT+ Identity (OLS Regression)
. 
. reg factordefgay victimorder##dvictimindex  age education income unemployed i.religion arab, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       300
                                                {txt}F(9, 24)          =  {res}     1.45
                                                {txt}Prob > F          = {res}    0.2213
                                                {txt}R-squared         = {res}    0.0166
                                                {txt}Root MSE          =    {res} .83726

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            factordefgay{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2} .1100317{col 38}{space 2}  .254837{col 49}{space 1}    0.43{col 58}{space 3}0.670{col 66}{space 4}-.4159261{col 79}{space 3} .6359895
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.0520226{col 38}{space 2} .1736009{col 49}{space 1}   -0.30{col 58}{space 3}0.767{col 66}{space 4}-.4103172{col 79}{space 3} .3062721
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .0180018{col 38}{space 2} .2332365{col 49}{space 1}    0.08{col 58}{space 3}0.939{col 66}{space 4}-.4633747{col 79}{space 3} .4993784
{txt}{space 24} {c |}
{space 21}age {c |}{col 26}{res}{space 2}-.0043778{col 38}{space 2} .0084195{col 49}{space 1}   -0.52{col 58}{space 3}0.608{col 66}{space 4}-.0217549{col 79}{space 3} .0129993
{txt}{space 15}education {c |}{col 26}{res}{space 2}-.0686698{col 38}{space 2} .1224095{col 49}{space 1}   -0.56{col 58}{space 3}0.580{col 66}{space 4}-.3213107{col 79}{space 3}  .183971
{txt}{space 18}income {c |}{col 26}{res}{space 2}-.0504686{col 38}{space 2}  .059398{col 49}{space 1}   -0.85{col 58}{space 3}0.404{col 66}{space 4}-.1730601{col 79}{space 3} .0721229
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .1196028{col 38}{space 2} .2153836{col 49}{space 1}    0.56{col 58}{space 3}0.584{col 66}{space 4}-.3249271{col 79}{space 3} .5641327
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2}-.2123451{col 38}{space 2} .1615801{col 49}{space 1}   -1.31{col 58}{space 3}0.201{col 66}{space 4}  -.54583{col 79}{space 3} .1211397
{txt}{space 20}arab {c |}{col 26}{res}{space 2} .0379091{col 38}{space 2} .1495651{col 49}{space 1}    0.25{col 58}{space 3}0.802{col 66}{space 4}-.2707779{col 79}{space 3} .3465962
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} .4888155{col 38}{space 2} .5608049{col 49}{space 1}    0.87{col 58}{space 3}0.392{col 66}{space 4}-.6686289{col 79}{space 3}  1.64626
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg factordefgay victimorder##dvictimindex revprotectgays revhumanrights closegay age education income unemployed i.religion arab, cluster(locale)

{txt}Linear regression                               Number of obs     = {res}       300
                                                {txt}F(12, 24)         =  {res}    23.44
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2959
                                                {txt}Root MSE          =    {res} .71217

{txt}{ralign 90:(Std. err. adjusted for {res:25} clusters in {res:locale})}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}            factordefgay{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.victimorder {c |}{col 26}{res}{space 2} .0866356{col 38}{space 2} .1414952{col 49}{space 1}    0.61{col 58}{space 3}0.546{col 66}{space 4}-.2053961{col 79}{space 3} .3786672
{txt}{space 10}1.dvictimindex {c |}{col 26}{res}{space 2}-.1404359{col 38}{space 2} .0979334{col 49}{space 1}   -1.43{col 58}{space 3}0.164{col 66}{space 4}-.3425606{col 79}{space 3} .0616887
{txt}{space 24} {c |}
victimorder#dvictimindex {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .0476931{col 38}{space 2} .1302761{col 49}{space 1}    0.37{col 58}{space 3}0.718{col 66}{space 4}-.2211835{col 79}{space 3} .3165697
{txt}{space 24} {c |}
{space 10}revprotectgays {c |}{col 26}{res}{space 2}-.2713368{col 38}{space 2} .0432308{col 49}{space 1}   -6.28{col 58}{space 3}0.000{col 66}{space 4}-.3605609{col 79}{space 3}-.1821128
{txt}{space 10}revhumanrights {c |}{col 26}{res}{space 2} -.304281{col 38}{space 2} .0511836{col 49}{space 1}   -5.94{col 58}{space 3}0.000{col 66}{space 4}-.4099188{col 79}{space 3}-.1986431
{txt}{space 16}closegay {c |}{col 26}{res}{space 2}-.1063226{col 38}{space 2} .0215066{col 49}{space 1}   -4.94{col 58}{space 3}0.000{col 66}{space 4}  -.15071{col 79}{space 3}-.0619352
{txt}{space 21}age {c |}{col 26}{res}{space 2}-.0049437{col 38}{space 2} .0071655{col 49}{space 1}   -0.69{col 58}{space 3}0.497{col 66}{space 4}-.0197325{col 79}{space 3} .0098451
{txt}{space 15}education {c |}{col 26}{res}{space 2} .1493546{col 38}{space 2}  .104435{col 49}{space 1}    1.43{col 58}{space 3}0.166{col 66}{space 4}-.0661887{col 79}{space 3} .3648979
{txt}{space 18}income {c |}{col 26}{res}{space 2}-.1256046{col 38}{space 2} .0654874{col 49}{space 1}   -1.92{col 58}{space 3}0.067{col 66}{space 4} -.260764{col 79}{space 3} .0095548
{txt}{space 14}unemployed {c |}{col 26}{res}{space 2} .0915436{col 38}{space 2} .1445971{col 49}{space 1}    0.63{col 58}{space 3}0.533{col 66}{space 4}-.2068901{col 79}{space 3} .3899773
{txt}{space 24} {c |}
{space 16}religion {c |}
{space 19}Shia  {c |}{col 26}{res}{space 2}-.0877746{col 38}{space 2} .1304515{col 49}{space 1}   -0.67{col 58}{space 3}0.507{col 66}{space 4}-.3570133{col 79}{space 3}  .181464
{txt}{space 20}arab {c |}{col 26}{res}{space 2}-.0332362{col 38}{space 2} .1204998{col 49}{space 1}   -0.28{col 58}{space 3}0.785{col 66}{space 4}-.2819356{col 79}{space 3} .2154632
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 2.013723{col 38}{space 2} .4318072{col 49}{space 1}    4.66{col 58}{space 3}0.000{col 66}{space 4} 1.122517{col 79}{space 3}  2.90493
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Empathy as Feelings of Closeness to LGBT+ People
. 
. histogram closegay if victimorder==0, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g9.gph
{res}{txt}file {bf:g9.gph} saved

{com}. histogram closegay if victimorder==1, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}1{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g10.gph
{res}{txt}file {bf:g10.gph} saved

{com}. graph combine "g9.gph" "g10.gph"
{res}{txt}
{com}.  
. cibar closegay, over(victimorder)
{res}{txt}
{com}. 
. *Table 13. Intensity of LGBT+ Support (Logit Regression)
. logit protectgaystrong dvictimindex gender age education income unemployed i.religion arab wave if revprotectgay>2,  vce(cluster locale)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-211.02238}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-186.71575}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-183.48788}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-183.44365}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -183.4436}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -183.4436}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:478}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:54.06}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-183.4436}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1307}

{txt}{ralign 82:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}protectgaystrong{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}dvictimindex {c |}{col 18}{res}{space 2}-.1613753{col 30}{space 2} .4023203{col 41}{space 1}   -0.40{col 50}{space 3}0.688{col 58}{space 4}-.9499086{col 71}{space 3}  .627158
{txt}{space 10}gender {c |}{col 18}{res}{space 2} .0777427{col 30}{space 2} .7338528{col 41}{space 1}    0.11{col 50}{space 3}0.916{col 58}{space 4}-1.360582{col 71}{space 3} 1.516068
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0065372{col 30}{space 2} .0253453{col 41}{space 1}    0.26{col 50}{space 3}0.796{col 58}{space 4}-.0431387{col 71}{space 3} .0562131
{txt}{space 7}education {c |}{col 18}{res}{space 2}-1.222854{col 30}{space 2} .4445556{col 41}{space 1}   -2.75{col 50}{space 3}0.006{col 58}{space 4}-2.094166{col 71}{space 3}-.3515406
{txt}{space 10}income {c |}{col 18}{res}{space 2} 1.096638{col 30}{space 2} .3682188{col 41}{space 1}    2.98{col 50}{space 3}0.003{col 58}{space 4} .3749429{col 71}{space 3} 1.818334
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2} -.000618{col 30}{space 2} .8826886{col 41}{space 1}   -0.00{col 50}{space 3}0.999{col 58}{space 4}-1.730656{col 71}{space 3}  1.72942
{txt}{space 16} {c |}
{space 8}religion {c |}
{space 11}Shia  {c |}{col 18}{res}{space 2}-2.221826{col 30}{space 2} 1.417245{col 41}{space 1}   -1.57{col 50}{space 3}0.117{col 58}{space 4}-4.999575{col 71}{space 3} .5559224
{txt}{space 12}arab {c |}{col 18}{res}{space 2} .4519194{col 30}{space 2} .3344912{col 41}{space 1}    1.35{col 50}{space 3}0.177{col 58}{space 4}-.2036712{col 71}{space 3}  1.10751
{txt}{space 12}wave {c |}{col 18}{res}{space 2} -.665174{col 30}{space 2} .5432932{col 41}{space 1}   -1.22{col 50}{space 3}0.221{col 58}{space 4}-1.730009{col 71}{space 3} .3996612
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} .2220694{col 30}{space 2} 1.403558{col 41}{space 1}    0.16{col 50}{space 3}0.874{col 58}{space 4}-2.528853{col 71}{space 3} 2.972992
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit humanrightstrong  dvictimindex gender age education income unemployed i.religion arab wave if revhumanrights>2,  vce(cluster locale) 

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-214.38899}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-190.83015}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-187.44371}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-187.30458}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-187.30361}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-187.30361}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:508}
{txt}{col 57}{lalign 13:Wald chi2({res:9})}{col 70} = {res}{ralign 6:44.40}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-187.30361}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1263}

{txt}{ralign 82:(Std. err. adjusted for {res:30} clusters in {res:locale})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}humanrightstrong{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}dvictimindex {c |}{col 18}{res}{space 2}-.2941503{col 30}{space 2} .5048725{col 41}{space 1}   -0.58{col 50}{space 3}0.560{col 58}{space 4}-1.283682{col 71}{space 3} .6953816
{txt}{space 10}gender {c |}{col 18}{res}{space 2} .0611604{col 30}{space 2} .6218516{col 41}{space 1}    0.10{col 50}{space 3}0.922{col 58}{space 4}-1.157646{col 71}{space 3} 1.279967
{txt}{space 13}age {c |}{col 18}{res}{space 2} .0267181{col 30}{space 2} .0218859{col 41}{space 1}    1.22{col 50}{space 3}0.222{col 58}{space 4}-.0161774{col 71}{space 3} .0696136
{txt}{space 7}education {c |}{col 18}{res}{space 2}-1.144601{col 30}{space 2} .3269356{col 41}{space 1}   -3.50{col 50}{space 3}0.000{col 58}{space 4}-1.785383{col 71}{space 3}-.5038186
{txt}{space 10}income {c |}{col 18}{res}{space 2} .1856948{col 30}{space 2} .2814123{col 41}{space 1}    0.66{col 50}{space 3}0.509{col 58}{space 4}-.3658631{col 71}{space 3} .7372527
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2}-1.775478{col 30}{space 2} 1.612866{col 41}{space 1}   -1.10{col 50}{space 3}0.271{col 58}{space 4}-4.936638{col 71}{space 3} 1.385682
{txt}{space 16} {c |}
{space 8}religion {c |}
{space 11}Shia  {c |}{col 18}{res}{space 2}-2.767546{col 30}{space 2} 1.260226{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-5.237544{col 71}{space 3}-.2975485
{txt}{space 12}arab {c |}{col 18}{res}{space 2} .6497558{col 30}{space 2} .4815311{col 41}{space 1}    1.35{col 50}{space 3}0.177{col 58}{space 4}-.2940279{col 71}{space 3} 1.593539
{txt}{space 12}wave {c |}{col 18}{res}{space 2}-1.109257{col 30}{space 2} .3372414{col 41}{space 1}   -3.29{col 50}{space 3}0.001{col 58}{space 4}-1.770238{col 71}{space 3}-.4482761
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 2.806263{col 30}{space 2} 1.866624{col 41}{space 1}    1.50{col 50}{space 3}0.133{col 58}{space 4}-.8522535{col 71}{space 3} 6.464779
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Figure 2. Adjusted for Imbalances
. 
. cem dvictimindex education income religion, treatment(victimorder)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}30
{txt}Number of matched strata: {res}14

           {txt}  0    1
      All  {res}450  450
{txt}  Matched  {res}344  450
{txt}Unmatched  {res}106    0


{txt}Multivariate L1 distance: {res}4.671e-16

{txt}Univariate imbalance:

                   L1     mean      min      25%      50%      75%      max
dvictimindex  {res}3.9e-16  2.2e-16        0        0        0        0        0
{txt}   education  {res}7.1e-16  5.8e-15        0        0        0        0        0
{txt}      income  {res}2.5e-16  6.2e-15        0        0        0        0        0
{txt}    religion  {res}6.0e-17  2.2e-16        0        0        0        0        0
{txt}
{com}. tab revprotectgay if cem_matched==1 & victimorder==0

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         30        8.72        8.72
{txt}somewhat disagree {c |}{res}        196       56.98       65.70
{txt}   somewhat agree {c |}{res}         70       20.35       86.05
{txt}   strongly agree {c |}{res}         48       13.95      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        344      100.00
{txt}
{com}. tab revprotectgay if cem_matched==1 & victimorder==1

{txt}Iraqi authorities {c |}
should do more to {c |}
          protect {c |}
   gay/homosexual {c |}
    people from v {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          6        1.33        1.33
{txt}somewhat disagree {c |}{res}        125       27.78       29.11
{txt}   somewhat agree {c |}{res}        303       67.33       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. tab revhumanrights if cem_matched==1 & victimorder==0

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         36       10.47       10.47
{txt}somewhat disagree {c |}{res}        165       47.97       58.43
{txt}   somewhat agree {c |}{res}         96       27.91       86.34
{txt}   strongly agree {c |}{res}         47       13.66      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        344      100.00
{txt}
{com}. tab revhumanrights if cem_matched==1 & victimorder==1

   {txt}Gay/homosexual {c |}
       people are {c |}
entitled to human {c |}
           rights {c |}
protections under {c |}
              Ira {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}          7        1.56        1.56
{txt}somewhat disagree {c |}{res}        129       28.67       30.22
{txt}   somewhat agree {c |}{res}        298       66.22       96.44
{txt}   strongly agree {c |}{res}         16        3.56      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        450      100.00
{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Mosul LGBT\Mosul LGBT Aug-Oct 2021\ISIS Victimization\CPS\CPS replication instructions\CPS replication log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Dec 2024, 14:51:40
{txt}{.-}
{smcl}
{txt}{sf}{ul off}